NP URI: www.nobleprog.com.ve/en/cc/generativeairobot Undefined array key "mysql_identifier_quote_character" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "mysql_identifier_quote_character" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 397 [function] => variable_get [args] => Array ( [0] => mysql_identifier_quote_character [1] => ` ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 329 [function] => setPrefix [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [default] => ) ) ) [3] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 349 [function] => __construct [class] => DatabaseConnection [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => mysql:host=localhost;port=3306;charset=utf8;dbname=hitrahr [1] => root [2] => asdf [3] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) [4] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1796 [function] => __construct [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) [pdo] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) ) [5] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1582 [function] => openConnection [class] => Database [type] => :: [args] => Array ( [0] => hitrahr [1] => default ) ) [6] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 2467 [function] => getConnection [class] => Database [type] => :: [args] => Array ( [0] => default ) ) [7] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 31 [function] => db_query [args] => Array ( [0] => SELECT entity_id FROM field_data_field_url_alias WHERE field_url_alias_value = :alias AND entity_type = 'taxonomy_term' AND language = :language [1] => Array ( [:alias] => cc [:language] => en ) ) ) [8] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 149 [function] => np_db_query [args] => Array ( [0] => hitrahr [1] => db_query [2] => SELECT entity_id FROM field_data_field_url_alias WHERE field_url_alias_value = :alias AND entity_type = 'taxonomy_term' AND language = :language [3] => Array ( [:alias] => cc [:language] => en ) ) ) [9] => Array ( [file] => /apps/nobleprog-website/routes.logic.php [line] => 75 [function] => category_validate_url_alias [args] => Array ( [0] => cc ) ) [10] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 86 [function] => check_for_module [args] => Array ( [0] => /en/cc/generativeairobot [1] => Array ( [0] => [1] => cc [2] => generativeairobot [3] => en ) ) ) [11] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [12] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ve/en/cc/generativeairobot Undefined array key "mysql_identifier_quote_character" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "mysql_identifier_quote_character" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 397 [function] => variable_get [args] => Array ( [0] => mysql_identifier_quote_character [1] => ` ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 329 [function] => setPrefix [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [default] => ) ) ) [3] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 349 [function] => __construct [class] => DatabaseConnection [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => mysql:host=localhost;port=3306;charset=utf8;dbname=common_fe [1] => root [2] => asdf [3] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) [4] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1796 [function] => __construct [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) [pdo] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) ) [5] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1582 [function] => openConnection [class] => Database [type] => :: [args] => Array ( [0] => common_fe [1] => default ) ) [6] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 2467 [function] => getConnection [class] => Database [type] => :: [args] => Array ( [0] => default ) ) [7] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 31 [function] => db_query [args] => Array ( [0] => SELECT * FROM price_formulas WHERE country_code = :country_code [1] => Array ( [:country_code] => ve ) ) ) [8] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 111 [function] => np_db_query [args] => Array ( [0] => common_fe [1] => db_query [2] => SELECT * FROM price_formulas WHERE country_code = :country_code [3] => Array ( [:country_code] => ve ) ) ) [9] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 93 [function] => get_formula [args] => Array ( [0] => ve ) ) [10] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 355 [function] => course_price_v2_formula [args] => Array ( ) ) [11] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 344 [function] => course_price_change_to_fe_p [args] => Array ( [0] => generativeairobot [1] => 28 [2] => [3] => 0 [4] => [5] => USD ) ) [12] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 316 [function] => course_price_get_default_price [args] => Array ( [0] => generativeairobot ) ) [13] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 15 [function] => course_price_get_price [args] => Array ( [0] => generativeairobot ) ) [14] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 23 [function] => course_price_virtual_event_price [args] => Array ( [0] => generativeairobot ) ) [15] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/generativeairobot ) ) [16] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [17] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [18] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ve/en/cc/generativeairobot Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5990 [adp] => 1250 [reduced_fdp] => [reduced_adp] => [days] => 4 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 28 [course_code] => generativeairobot [venue_id] => ve_10638169 [vfdc] => 250.00 [vadc] => 75.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 23 [function] => course_price_virtual_event_price [args] => Array ( [0] => generativeairobot ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/generativeairobot ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ve/en/cc/generativeairobot Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5990 [adp] => 1250 [reduced_fdp] => [reduced_adp] => [days] => 4 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 28 [course_code] => generativeairobot [venue_id] => ve_10638169 [vfdc] => 250.00 [vadc] => 75.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 23 [function] => course_price_virtual_event_price [args] => Array ( [0] => generativeairobot ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/generativeairobot ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ve/en/cc/generativeairobot Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5990 [adp] => 1250 [reduced_fdp] => [reduced_adp] => [days] => 4 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 28 [course_code] => generativeairobot [venue_id] => ve_10638169 [vfdc] => 250.00 [vadc] => 75.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 23 [function] => course_price_virtual_event_price [args] => Array ( [0] => generativeairobot ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/generativeairobot ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ve/en/cc/generativeairobot Cannot modify header information - headers already sent by (output started at /apps/nobleprog-website/_index.php:16) /apps/nobleprog-website/modules/course/course.php:119 Array ( [0] => Array ( [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Cannot modify header information - headers already sent by (output started at /apps/nobleprog-website/_index.php:16) [2] => /apps/nobleprog-website/modules/course/course.php [3] => 119 ) ) [1] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 119 [function] => header [args] => Array ( [0] => X-CSRF-Token:Tm9ibGVQcm9nMTcxNjAyMzk1OQ== ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 82 [function] => course_generate_csrf_token [args] => Array ( ) ) [3] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 31 [function] => course_render [args] => Array ( [0] => Array ( [course_code] => generativeairobot [hr_nid] => 479011 [title] => Generative AI in Robotics: Creating Autonomous Solutions [requirements] =>

Audience

[overview] =>

Generative AI is a cutting-edge field of AI that focuses on creating systems that can generate new, complex patterns and behaviors.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level robotics engineers and AI researchers who wish to design and implement autonomous robotic systems using Generative AI techniques.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level to advanced-level robotics engineers and AI researchers who wish to design and implement autonomous robotic systems using Generative AI techniques.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Generative AI in Robotics

Designing AI-Generated Robots

AI in Robotic Perception and Decision-Making

Robotics in Manufacturing and Industry

AI Robotics in Service and Healthcare

Challenges and Future Directions

Capstone Project

Summary and Next Steps

[language] => en [duration] => 28 [status] => published [changed] => 1714523875 [source_title] => Generative AI in Robotics: Creating Autonomous Solutions [source_language] => en [cert_code] => [weight] => -1010 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) ) [1] => Array ( [0] => stdClass Object ( [tid] => 4879 [alias] => generative-ai-training [name] => Generative AI [english_name] => Generative AI [consulting_option] => available ) ) [2] => generativeairobot [3] => Array ( [outlines] => Array ( [langchain] => stdClass Object ( [course_code] => langchain [hr_nid] => 476371 [title] => LangChain: Building AI-Powered Applications [requirements] =>

Audience

[overview] =>

LangChain is an open-source framework designed to facilitate the development of applications using large language models (LLMs).

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers and software engineers who wish to build AI-powered applications using the LangChain framework.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level developers and software engineers who wish to build AI-powered applications using the LangChain framework.

By the end of this training, participants will be able to:

[outline] =>

Introduction to LangChain

Understanding Large Language Models (LLMs)

LangChain Components and Architecture

Integrating LangChain with LLMs

Building Modular Applications

Practical Exercises with LangChain

Advanced LangChain Features

Best Practices and Patterns

Troubleshooting

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1712088649 [source_title] => LangChain: Building AI-Powered Applications [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => langchain ) [langchainfun] => stdClass Object ( [course_code] => langchainfun [hr_nid] => 476375 [title] => LangChain Fundamentals [requirements] =>

Audience

[overview] =>

LangChain is an open-source framework that simplifies the integration of large language models (LLMs) into applications.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers and software engineers who wish to learn the core concepts and architecture of LangChain and gain the practical skills for building AI-powered applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level developers and software engineers who wish to learn the core concepts and architecture of LangChain and gain the practical skills for building AI-powered applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction to LangChain

Setting Up the Environment

Core Concepts of LangChain

Working with Large Language Models (LLMs)

Developing with LangChain

Troubleshooting

Conclusion and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1712091756 [source_title] => LangChain Fundamentals [source_language] => en [cert_code] => [weight] => -1002 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => langchainfun ) [slms] => stdClass Object ( [course_code] => slms [hr_nid] => 479495 [title] => Small Language Models (SLMs): Applications and Innovations [requirements] =>

Audience

[overview] =>

Small Language Models (SLMs) are a cutting-edge subset of AI that enables efficient language processing on devices with limited computational resources.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level data scientists and developers who wish to implement and leverage Small Language Models in various applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level data scientists and developers who wish to implement and leverage Small Language Models in various applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Small Language Models (SLMs)

Technical Foundations

SLMs in Natural Language Processing

Real-world Applications of SLMs

Case Studies

Future Directions

Hands-on Workshops

Capstone Project

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1715280132 [source_title] => Small Language Models (SLMs): Applications and Innovations [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slms ) [slmsdsa] => stdClass Object ( [course_code] => slmsdsa [hr_nid] => 479651 [title] => Small Language Models (SLMs) for Domain-Specific Applications [requirements] =>

Audience

[overview] =>

Small Language Models (SLMs) are a cutting-edge subset of AI that enables efficient language processing on devices with limited computational resources.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists and machine learning engineers who wish to create and apply small language models tailored for specific domains such as legal, medical, and technical fields.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level data scientists and machine learning engineers who wish to create and apply small language models tailored for specific domains such as legal, medical, and technical fields.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Domain-Specific Language Models

Data Curation and Preprocessing

Model Training and Fine-Tuning

Evaluation Metrics and Model Performance

Deployment Strategies

Legal Domain Focus

Medical Domain Focus

Technical Domain Focus

Project and Assessment

Summary and Next Steps

[language] => en [duration] => 28 [status] => published [changed] => 1715281386 [source_title] => Small Language Models (SLMs) for Domain-Specific Applications [source_language] => en [cert_code] => [weight] => -1002 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slmsdsa ) [slmseeai] => stdClass Object ( [course_code] => slmseeai [hr_nid] => 479667 [title] => Small Language Models (SLMs): Developing Energy-Efficient AI [requirements] =>

Audience

[overview] =>

Small Language Models (SLMs) are efficient alternatives to larger models, offering comparable performance with significantly reduced computational and energy requirements.

This instructor-led, live training (online or onsite) is aimed at advanced-level machine learning engineers and AI researchers who wish to develop energy-efficient AI solutions with small language models that are both powerful and environmentally friendly.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at advanced-level machine learning engineers and AI researchers who wish to develop energy-efficient AI solutions with small language models that are both powerful and environmentally friendly.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Energy-Efficient AI

Compact Model Architectures

Optimization and Compression Techniques

Hardware Considerations for AI

Green Coding Practices

Renewable Energy and AI

Lifecycle Assessment of AI Systems

Policy and Regulation for Sustainable AI

Project and Assessment

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1715307649 [source_title] => Small Language Models (SLMs): Developing Energy-Efficient AI [source_language] => en [cert_code] => [weight] => -1004 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slmseeai ) [slmshai] => stdClass Object ( [course_code] => slmshai [hr_nid] => 479659 [title] => Small Language Models (SLMs) for Human-AI Interactions [requirements] =>

Audience

[overview] =>

Small Language Models (SLMs) are compact yet powerful tools for enabling sophisticated human-AI interactions in various applications, including conversational AI and customer service bots.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists, machine learning and AI researchers who wish to create engaging and efficient AI-powered conversational experiences with small language models.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level data scientists, machine learning and AI researchers who wish to create engaging and efficient AI-powered conversational experiences with small language models.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Conversational AI and Small Language Models (SLMs)

Designing Conversational Flows

Building Customer Service Bots

Training SLMs for Interaction

Evaluating Interaction Quality

Voice-Enabled and Multimodal Interactions

Personalization and Contextual Understanding

Ethical Considerations and Bias Mitigation

Deployment and Scaling

Capstone Project

Final Assessment

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1715283400 [source_title] => Small Language Models (SLMs) for Human-AI Interactions [source_language] => en [cert_code] => [weight] => -1003 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slmshai ) [slmsodai] => stdClass Object ( [course_code] => slmsodai [hr_nid] => 479671 [title] => Small Language Models (SLMs) for On-Device AI [requirements] =>

Audience

[overview] =>

Small Language Models (SLMs) are efficient and versatile AI tools that can be implemented on a variety of devices, from smartphones to IoT devices, enabling intelligent on-device applications.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IT professionals who wish to deploy small language models directly onto devices with limited processing capabilities, opening up possibilities for innovative applications in various sectors.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level IT professionals who wish to deploy small language models directly onto devices with limited processing capabilities, opening up possibilities for innovative applications in various sectors.

By the end of this training, participants will be able to:

[outline] =>

Introduction to On-Device AI

Model Optimization for On-Device Deployment

Platform-Specific AI Tools and Frameworks

Real-Time Inference and Edge Computing

Power Management and Battery Life Considerations

Security and Privacy in On-Device AI

User Experience and Interaction Design

Scalability and Maintenance

Project and Assessment

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1715323768 [source_title] => Small Language Models (SLMs) for On-Device AI [source_language] => en [cert_code] => [weight] => -1005 [excluded_sites] => hitrait [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slmsodai ) [geminiai] => stdClass Object ( [course_code] => geminiai [hr_nid] => 476043 [title] => Introduction to Google Gemini AI [requirements] =>

Audience


 

[overview] =>

Google Gemini AI is a cutting-edge large language model that offers advanced AI capabilities, such as natural language understanding, text generation, and semantic search, enabling developers to create more intuitive and responsive AI-driven applications.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to integrate AI functionalities into their applications using Google Gemini AI.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to integrate AI functionalities into their applications using Google Gemini AI.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AI and Google Gemini

Understanding Large Language Models (LLMs)

Getting Started with Google Gemini

Working with Gemini Models

Practical Applications of Gemini AI

Advanced Features and Customization

Project - Building an AI Code Buddy

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1711952394 [source_title] => Introduction to Google Gemini AI [source_language] => en [cert_code] => [weight] => -1005 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiai ) [geminiaiforcontentcreation] => stdClass Object ( [course_code] => geminiaiforcontentcreation [hr_nid] => 476187 [title] => Google Gemini AI for Content Creation [requirements] =>

Audience

[overview] =>

Google Gemini AI is a transformative tool for content creators, offering capabilities that streamline the creation process of content for various mediums, such as web content, marketing materials, and multimedia projects.

This instructor-led, live training (online or onsite) is aimed at intermediate-level content creators who wish to utilize Google Gemini AI to enhance their content quality and efficiency.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level content creators who wish to utilize Google Gemini AI to enhance their content quality and efficiency.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AI-Powered Content Creation

Setting Up Google Gemini for Content Projects

Automating Content Generation with Gemini AI

Personalizing Content with Gemini AI

SEO Optimization with Gemini AI

Analyzing Content Performance with Gemini AI

Project - Creating a Content Campaign

Conclusion and Future of AI in Content Creation

[language] => en [duration] => 14 [status] => published [changed] => 1711653905 [source_title] => Google Gemini AI for Content Creation [source_language] => en [cert_code] => [weight] => -1007 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiaiforcontentcreation ) [geminiaiforcustomerservice] => stdClass Object ( [course_code] => geminiaiforcustomerservice [hr_nid] => 476047 [title] => Google Gemini AI for Transformative Customer Service [requirements] =>

Audience

[overview] =>

Google Gemini AI is a versatile tool designed to revolutionize customer service interactions by leveraging advanced machine learning algorithms. It enhances real-time communication across various platforms such as live chat, email support, and social media engagement. By automating routine tasks and providing actionable insights from customer data, Google Gemini AI significantly improves the overall customer experience and operational efficiency.

This instructor-led, live training (online or onsite) is aimed at intermediate-level customer service professionals who wish to implement Google Gemini AI in their customer service operations.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level customer service professionals who wish to implement Google Gemini AI in their customer service operations.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AI in Customer Service

Setting Up Google Gemini for Customer Interactions

Automating Customer Support with Gemini AI

Enhancing Customer Engagement

Analyzing Customer Feedback with Gemini AI

Case Studies and Best Practices

Project - Implementing Gemini AI Chatbot

Conclusion and Future Trends

[language] => en [duration] => 14 [status] => published [changed] => 1711648466 [source_title] => Google Gemini AI for Transformative Customer Service [source_language] => en [cert_code] => [weight] => -1006 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiaiforcustomerservice ) [geminiaifordataanalysis] => stdClass Object ( [course_code] => geminiaifordataanalysis [hr_nid] => 476191 [title] => Google Gemini AI for Data Analysis [requirements] =>

Audience

[overview] =>

Google Gemini AI is a cutting-edge tool that provides users with natural language and visual interfaces to enhance data exploration, analysis, visualization, and communication.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level data analysts and business professionals who wish to perform complex data analysis tasks more intuitively across various industries using Google Gemini AI.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level data analysts and business professionals who wish to perform complex data analysis tasks more intuitively across various industries using Google Gemini AI.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Google Gemini AI

Connecting Data Sources

Exploring Data with Gemini AI

Data Analysis and Insights

Data Visualization

Communicating Insights

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1711656398 [source_title] => Google Gemini AI for Data Analysis [source_language] => en [cert_code] => [weight] => -1008 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiaifordataanalysis ) [generativeaillm] => stdClass Object ( [course_code] => generativeaillm [hr_nid] => 463251 [title] => Generative AI with Large Language Models (LLMs) [requirements] =>

Audience

[overview] =>

Generative AI is a type of AI that can create original content such as text, images, music, and code. Large language models (LLMs) are powerful neural networks that can process and generate natural language. 

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to learn how to use generative AI with LLMs for various tasks and domains.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level developers who wish to learn how to use generative AI with LLMs for various tasks and domains.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Generative AI

Transformer Architecture and LLMs

Scaling Laws and Optimization

Training and Fine-Tuning LLMs

Deploying and Using LLMs

Ethics and Future of Generative AI

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1709073362 [source_title] => Generative AI with Large Language Models (LLMs) [source_language] => en [cert_code] => [weight] => -1004 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => generativeaillm ) [llamaindex] => stdClass Object ( [course_code] => llamaindex [hr_nid] => 476587 [title] => LlamaIndex: Enhancing Contextual AI [requirements] =>

Audience

[overview] =>

LlamaIndex is an open-source data framework designed for applications that use Large Language Models (LLMs) and benefit from context augmentation. It's particularly useful for systems known as Retrieval-Augmented Generation (RAG) systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI researchers, machine learning professionals, and data scientists who wish to use LlamaIndex to enhance the capabilities of AI models, making them more accurate and reliable for various applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level AI researchers, machine learning professionals, and data scientists who wish to use LlamaIndex to enhance the capabilities of AI models, making them more accurate and reliable for various applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction to LlamaIndex and Context Augmentation

Setting Up LlamaIndex

Data Indexing and Access

Integrating LlamaIndex with LLMs

Application Scenarios and Case Studies

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1712369760 [source_title] => LlamaIndex: Enhancing Contextual AI [source_language] => en [cert_code] => [weight] => -1009 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => llamaindex ) [llamaindexdev] => stdClass Object ( [course_code] => llamaindexdev [hr_nid] => 476715 [title] => LlamaIndex: Developing LLM Powered Applications [requirements] =>

Audience

[overview] =>

LlamaIndex is a powerful indexing tool designed to enhance the capabilities of Large Language Models (LLMs) by allowing them to retrieve and utilize custom data sets effectively.

This instructor-led, live training (online or onsite) is aimed at beginner-level to advanced-level developers and data scientists who wish to master LlamaIndex for developing innovative LLM-powered applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to advanced-level developers and data scientists who wish to master LlamaIndex for developing innovative LLM-powered applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction to LlamaIndex

LlamaIndex in Action

Advanced LlamaIndex Features

Application Development with LlamaIndex

Deployment and Scaling

Ethical and Practical Considerations

Summary and Next Steps

[language] => en [duration] => 42 [status] => published [changed] => 1712622902 [source_title] => LlamaIndex: Developing LLM Powered Applications [source_language] => en [cert_code] => [weight] => -1010 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => llamaindexdev ) [llm] => stdClass Object ( [course_code] => llm [hr_nid] => 462191 [title] => Introduction to Large Language Models (LLMs) [requirements] =>

Audience

[overview] =>

Large Language Models (LLMs) are deep neural network models that can generate natural language texts based on a given input or context. They are trained on large amounts of text data from various domains and sources, and they can capture the syntactic and semantic patterns of natural language. LLMs have achieved impressive results on various natural language tasks such as text summarization, question answering, text generation, and more.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Understanding LLMs

Getting Started

Working with LLMs

Text Summarization

Question Answering

Text Generation

Integrating LLMs with Other Frameworks and Platforms

Troubleshooting

Summary and Next Steps

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Course Outline

Introduction to Generative AI in Robotics

  • Understanding Generative AI
  • Core concepts in robotics and automation
  • Overview of AI-driven robotic systems

Designing AI-Generated Robots

  • Generative design processes for robotics
  • Simulation and virtual testing of robotic models
  • Case studies of generative robotics in action

AI in Robotic Perception and Decision-Making

  • Sensory data processing with AI
  • Machine learning for robotic cognition
  • Workshop: Programming AI for robotic decision-making

Robotics in Manufacturing and Industry

  • Automation and AI in industrial settings
  • Collaborative robots (cobots) and human-robot interaction
  • Impact assessment of AI robotics on workforce and productivity

AI Robotics in Service and Healthcare

  • Service robots in retail, hospitality, and customer service
  • AI-driven robots in healthcare and assisted living
  • Ethical considerations in service robotics

Challenges and Future Directions

  • Addressing technical and ethical challenges in AI robotics
  • The future landscape of robotics in society
  • Preparing for the next wave of AI advancements in robotics

Capstone Project

  • Designing an AI-driven robotic solution for a real-world problem
  • Implementing and testing the robotic prototype
  • Critical analysis and feedback

Summary and Next Steps

Requirements

  • An understanding of robotics fundamentals
  • Experience with programming in Python or C++
  • Familiarity with basic AI concepts

Audience

  • Robotics engineers
  • AI researchers
 28 Hours

Number of participants



Price per participant

Open Training Courses require 5+ participants.

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Generative AI
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[category_overview] =>

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[overview] =>

Small Language Models (SLMs) are efficient and versatile AI tools that can be implemented on a variety of devices, from smartphones to IoT devices, enabling intelligent on-device applications.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IT professionals who wish to deploy small language models directly onto devices with limited processing capabilities, opening up possibilities for innovative applications in various sectors.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level IT professionals who wish to deploy small language models directly onto devices with limited processing capabilities, opening up possibilities for innovative applications in various sectors.

By the end of this training, participants will be able to:

[outline] =>

Introduction to On-Device AI

Model Optimization for On-Device Deployment

Platform-Specific AI Tools and Frameworks

Real-Time Inference and Edge Computing

Power Management and Battery Life Considerations

Security and Privacy in On-Device AI

User Experience and Interaction Design

Scalability and Maintenance

Project and Assessment

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1715323768 [source_title] => Small Language Models (SLMs) for On-Device AI [source_language] => en [cert_code] => [weight] => -1005 [excluded_sites] => hitrait [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slmsodai ) [geminiai] => stdClass Object ( [course_code] => geminiai [hr_nid] => 476043 [title] => Introduction to Google Gemini AI [requirements] =>

Audience


 

[overview] =>

Google Gemini AI is a cutting-edge large language model that offers advanced AI capabilities, such as natural language understanding, text generation, and semantic search, enabling developers to create more intuitive and responsive AI-driven applications.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to integrate AI functionalities into their applications using Google Gemini AI.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to integrate AI functionalities into their applications using Google Gemini AI.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AI and Google Gemini

Understanding Large Language Models (LLMs)

Getting Started with Google Gemini

Working with Gemini Models

Practical Applications of Gemini AI

Advanced Features and Customization

Project - Building an AI Code Buddy

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1711952394 [source_title] => Introduction to Google Gemini AI [source_language] => en [cert_code] => [weight] => -1005 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiai ) [geminiaiforcontentcreation] => stdClass Object ( [course_code] => geminiaiforcontentcreation [hr_nid] => 476187 [title] => Google Gemini AI for Content Creation [requirements] =>

Audience

[overview] =>

Google Gemini AI is a transformative tool for content creators, offering capabilities that streamline the creation process of content for various mediums, such as web content, marketing materials, and multimedia projects.

This instructor-led, live training (online or onsite) is aimed at intermediate-level content creators who wish to utilize Google Gemini AI to enhance their content quality and efficiency.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level content creators who wish to utilize Google Gemini AI to enhance their content quality and efficiency.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AI-Powered Content Creation

Setting Up Google Gemini for Content Projects

Automating Content Generation with Gemini AI

Personalizing Content with Gemini AI

SEO Optimization with Gemini AI

Analyzing Content Performance with Gemini AI

Project - Creating a Content Campaign

Conclusion and Future of AI in Content Creation

[language] => en [duration] => 14 [status] => published [changed] => 1711653905 [source_title] => Google Gemini AI for Content Creation [source_language] => en [cert_code] => [weight] => -1007 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiaiforcontentcreation ) [geminiaiforcustomerservice] => stdClass Object ( [course_code] => geminiaiforcustomerservice [hr_nid] => 476047 [title] => Google Gemini AI for Transformative Customer Service [requirements] =>

Audience

[overview] =>

Google Gemini AI is a versatile tool designed to revolutionize customer service interactions by leveraging advanced machine learning algorithms. It enhances real-time communication across various platforms such as live chat, email support, and social media engagement. By automating routine tasks and providing actionable insights from customer data, Google Gemini AI significantly improves the overall customer experience and operational efficiency.

This instructor-led, live training (online or onsite) is aimed at intermediate-level customer service professionals who wish to implement Google Gemini AI in their customer service operations.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level customer service professionals who wish to implement Google Gemini AI in their customer service operations.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AI in Customer Service

Setting Up Google Gemini for Customer Interactions

Automating Customer Support with Gemini AI

Enhancing Customer Engagement

Analyzing Customer Feedback with Gemini AI

Case Studies and Best Practices

Project - Implementing Gemini AI Chatbot

Conclusion and Future Trends

[language] => en [duration] => 14 [status] => published [changed] => 1711648466 [source_title] => Google Gemini AI for Transformative Customer Service [source_language] => en [cert_code] => [weight] => -1006 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiaiforcustomerservice ) [geminiaifordataanalysis] => stdClass Object ( [course_code] => geminiaifordataanalysis [hr_nid] => 476191 [title] => Google Gemini AI for Data Analysis [requirements] =>

Audience

[overview] =>

Google Gemini AI is a cutting-edge tool that provides users with natural language and visual interfaces to enhance data exploration, analysis, visualization, and communication.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level data analysts and business professionals who wish to perform complex data analysis tasks more intuitively across various industries using Google Gemini AI.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level data analysts and business professionals who wish to perform complex data analysis tasks more intuitively across various industries using Google Gemini AI.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Google Gemini AI

Connecting Data Sources

Exploring Data with Gemini AI

Data Analysis and Insights

Data Visualization

Communicating Insights

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1711656398 [source_title] => Google Gemini AI for Data Analysis [source_language] => en [cert_code] => [weight] => -1008 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiaifordataanalysis ) [generativeaillm] => stdClass Object ( [course_code] => generativeaillm [hr_nid] => 463251 [title] => Generative AI with Large Language Models (LLMs) [requirements] =>

Audience

[overview] =>

Generative AI is a type of AI that can create original content such as text, images, music, and code. Large language models (LLMs) are powerful neural networks that can process and generate natural language. 

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to learn how to use generative AI with LLMs for various tasks and domains.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level developers who wish to learn how to use generative AI with LLMs for various tasks and domains.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Generative AI

Transformer Architecture and LLMs

Scaling Laws and Optimization

Training and Fine-Tuning LLMs

Deploying and Using LLMs

Ethics and Future of Generative AI

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1709073362 [source_title] => Generative AI with Large Language Models (LLMs) [source_language] => en [cert_code] => [weight] => -1004 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => generativeaillm ) [llamaindex] => stdClass Object ( [course_code] => llamaindex [hr_nid] => 476587 [title] => LlamaIndex: Enhancing Contextual AI [requirements] =>

Audience

[overview] =>

LlamaIndex is an open-source data framework designed for applications that use Large Language Models (LLMs) and benefit from context augmentation. It's particularly useful for systems known as Retrieval-Augmented Generation (RAG) systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI researchers, machine learning professionals, and data scientists who wish to use LlamaIndex to enhance the capabilities of AI models, making them more accurate and reliable for various applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level AI researchers, machine learning professionals, and data scientists who wish to use LlamaIndex to enhance the capabilities of AI models, making them more accurate and reliable for various applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction to LlamaIndex and Context Augmentation

Setting Up LlamaIndex

Data Indexing and Access

Integrating LlamaIndex with LLMs

Application Scenarios and Case Studies

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1712369760 [source_title] => LlamaIndex: Enhancing Contextual AI [source_language] => en [cert_code] => [weight] => -1009 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => llamaindex ) [llamaindexdev] => stdClass Object ( [course_code] => llamaindexdev [hr_nid] => 476715 [title] => LlamaIndex: Developing LLM Powered Applications [requirements] =>

Audience

[overview] =>

LlamaIndex is a powerful indexing tool designed to enhance the capabilities of Large Language Models (LLMs) by allowing them to retrieve and utilize custom data sets effectively.

This instructor-led, live training (online or onsite) is aimed at beginner-level to advanced-level developers and data scientists who wish to master LlamaIndex for developing innovative LLM-powered applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to advanced-level developers and data scientists who wish to master LlamaIndex for developing innovative LLM-powered applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction to LlamaIndex

LlamaIndex in Action

Advanced LlamaIndex Features

Application Development with LlamaIndex

Deployment and Scaling

Ethical and Practical Considerations

Summary and Next Steps

[language] => en [duration] => 42 [status] => published [changed] => 1712622902 [source_title] => LlamaIndex: Developing LLM Powered Applications [source_language] => en [cert_code] => [weight] => -1010 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => llamaindexdev ) [llm] => stdClass Object ( [course_code] => llm [hr_nid] => 462191 [title] => Introduction to Large Language Models (LLMs) [requirements] =>

Audience

[overview] =>

Large Language Models (LLMs) are deep neural network models that can generate natural language texts based on a given input or context. They are trained on large amounts of text data from various domains and sources, and they can capture the syntactic and semantic patterns of natural language. LLMs have achieved impressive results on various natural language tasks such as text summarization, question answering, text generation, and more.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Understanding LLMs

Getting Started

Working with LLMs

Text Summarization

Question Answering

Text Generation

Integrating LLMs with Other Frameworks and Platforms

Troubleshooting

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1700095996 [source_title] => Introduction to Large Language Models (LLMs) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => llm ) ) [codes] => Array ( [0] => langchain [1] => langchainfun [2] => slms [3] => slmsdsa [4] => slmseeai [5] => slmshai [6] => slmsodai [7] => geminiai [8] => geminiaiforcontentcreation [9] => geminiaiforcustomerservice [10] => geminiaifordataanalysis [11] => generativeaillm [12] => llamaindex [13] => llamaindexdev [14] => llm ) ) [4] => Array ( [regions] => Array ( [ve_1805] => Array ( [tid] => ve_1805 [title] => Caracas [sales_area] => ve_venezuela [venues] => Array ( [ve_10638169] => Array ( [vid] => ve_10638169 [title] => Caracas - Centro Lido [vfdc] => 250.00 [prices] => Array ( [1] => Array ( [remote guaranteed] => 5990 [classroom guaranteed] => 6990 [remote guaranteed per delegate] => 5990 [delegates] => 1 [adp] => 1250 [classroom guaranteed per delegate] => 6990 ) [2] => Array ( [remote guaranteed] => 7240 [classroom guaranteed] => 8540 [remote guaranteed per delegate] => 3620 [delegates] => 2 [adp] => 1250 [classroom guaranteed per delegate] => 4270 ) [3] => Array ( [remote guaranteed] => 8490 [classroom guaranteed] => 10089 [remote guaranteed per delegate] => 2830 [delegates] => 3 [adp] => 1250 [classroom guaranteed per delegate] => 3363 ) [4] => Array ( [remote guaranteed] => 9740 [classroom guaranteed] => 11640 [remote guaranteed per delegate] => 2435 [delegates] => 4 [adp] => 1250 [classroom guaranteed per delegate] => 2910 ) [5] => Array ( [remote guaranteed] => 10990 [classroom guaranteed] => 13190 [remote guaranteed per delegate] => 2198 [delegates] => 5 [adp] => 1250 [classroom guaranteed per delegate] => 2638 ) [6] => Array ( [remote guaranteed] => 12240 [classroom guaranteed] => 14742 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delegate] => 5990 [adp] => 1250 ) [2] => Array ( [remote guaranteed] => 7240 [remote guaranteed per delegate] => 3620 [adp] => 1250 ) [3] => Array ( [remote guaranteed] => 8490 [remote guaranteed per delegate] => 2830 [adp] => 1250 ) [4] => Array ( [remote guaranteed] => 9740 [remote guaranteed per delegate] => 2435 [adp] => 1250 ) [5] => Array ( [remote guaranteed] => 10990 [remote guaranteed per delegate] => 2198 [adp] => 1250 ) [6] => Array ( [remote guaranteed] => 12240 [remote guaranteed per delegate] => 2040 [adp] => 1250 ) [7] => Array ( [remote guaranteed] => 13489 [remote guaranteed per delegate] => 1927 [adp] => 1250 ) [8] => Array ( [remote guaranteed] => 14744 [remote guaranteed per delegate] => 1843 [adp] => 1250 ) [9] => Array ( [remote guaranteed] => 15993 [remote guaranteed per delegate] => 1777 [adp] => 1250 ) [10] => Array ( [remote guaranteed] => 17240 [remote guaranteed per delegate] => 1724 [adp] => 1250 ) ) [currency] => USD ) [5] => Array ( ) [6] => Array ( ) [7] => 0 [8] => 1 [9] => 1 [10] => ) ) [7] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/generativeairobot ) ) [8] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [9] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [10] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ve/en/cc/generativeairobot Undefined array key "devel_domain" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "devel_domain" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 99 [function] => variable_get [args] => Array ( [0] => devel_domain [1] => ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7_block.inc [line] => 34 [function] => islc_get_site_list [args] => Array ( ) ) [3] => Array ( [file] => /apps/nobleprog-website/nptemplates/default.php [line] => 265 [function] => islc7_sites_links_array_v3 [args] => Array ( ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 85 [args] => Array ( [0] => /apps/nobleprog-website/nptemplates/default.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 31 [function] => course_render [args] => Array ( [0] => Array ( [course_code] => generativeairobot [hr_nid] => 479011 [title] => Generative AI in Robotics: Creating Autonomous Solutions [requirements] =>

Audience

[overview] =>

Generative AI is a cutting-edge field of AI that focuses on creating systems that can generate new, complex patterns and behaviors.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level robotics engineers and AI researchers who wish to design and implement autonomous robotic systems using Generative AI techniques.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level to advanced-level robotics engineers and AI researchers who wish to design and implement autonomous robotic systems using Generative AI techniques.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Generative AI in Robotics

Designing AI-Generated Robots

AI in Robotic Perception and Decision-Making

Robotics in Manufacturing and Industry

AI Robotics in Service and Healthcare

Challenges and Future Directions

Capstone Project

Summary and Next Steps

[language] => en [duration] => 28 [status] => published [changed] => 1714523875 [source_title] => Generative AI in Robotics: Creating Autonomous Solutions [source_language] => en [cert_code] => [weight] => -1010 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) ) [1] => Array ( [0] => stdClass Object ( [tid] => 4879 [alias] => generative-ai-training [name] => Generative AI [english_name] => Generative AI [consulting_option] => available ) ) [2] => generativeairobot [3] => Array ( [outlines] => Array ( [langchain] => stdClass Object ( [course_code] => langchain [hr_nid] => 476371 [title] => LangChain: Building AI-Powered Applications [requirements] =>

Audience

[overview] =>

LangChain is an open-source framework designed to facilitate the development of applications using large language models (LLMs).

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers and software engineers who wish to build AI-powered applications using the LangChain framework.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level developers and software engineers who wish to build AI-powered applications using the LangChain framework.

By the end of this training, participants will be able to:

[outline] =>

Introduction to LangChain

Understanding Large Language Models (LLMs)

LangChain Components and Architecture

Integrating LangChain with LLMs

Building Modular Applications

Practical Exercises with LangChain

Advanced LangChain Features

Best Practices and Patterns

Troubleshooting

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1712088649 [source_title] => LangChain: Building AI-Powered Applications [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => langchain ) [langchainfun] => stdClass Object ( [course_code] => langchainfun [hr_nid] => 476375 [title] => LangChain Fundamentals [requirements] =>

Audience

[overview] =>

LangChain is an open-source framework that simplifies the integration of large language models (LLMs) into applications.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers and software engineers who wish to learn the core concepts and architecture of LangChain and gain the practical skills for building AI-powered applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level developers and software engineers who wish to learn the core concepts and architecture of LangChain and gain the practical skills for building AI-powered applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction to LangChain

Setting Up the Environment

Core Concepts of LangChain

Working with Large Language Models (LLMs)

Developing with LangChain

Troubleshooting

Conclusion and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1712091756 [source_title] => LangChain Fundamentals [source_language] => en [cert_code] => [weight] => -1002 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => langchainfun ) [slms] => stdClass Object ( [course_code] => slms [hr_nid] => 479495 [title] => Small Language Models (SLMs): Applications and Innovations [requirements] =>

Audience

[overview] =>

Small Language Models (SLMs) are a cutting-edge subset of AI that enables efficient language processing on devices with limited computational resources.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level data scientists and developers who wish to implement and leverage Small Language Models in various applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level data scientists and developers who wish to implement and leverage Small Language Models in various applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Small Language Models (SLMs)

Technical Foundations

SLMs in Natural Language Processing

Real-world Applications of SLMs

Case Studies

Future Directions

Hands-on Workshops

Capstone Project

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1715280132 [source_title] => Small Language Models (SLMs): Applications and Innovations [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slms ) [slmsdsa] => stdClass Object ( [course_code] => slmsdsa [hr_nid] => 479651 [title] => Small Language Models (SLMs) for Domain-Specific Applications [requirements] =>

Audience

[overview] =>

Small Language Models (SLMs) are a cutting-edge subset of AI that enables efficient language processing on devices with limited computational resources.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists and machine learning engineers who wish to create and apply small language models tailored for specific domains such as legal, medical, and technical fields.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level data scientists and machine learning engineers who wish to create and apply small language models tailored for specific domains such as legal, medical, and technical fields.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Domain-Specific Language Models

Data Curation and Preprocessing

Model Training and Fine-Tuning

Evaluation Metrics and Model Performance

Deployment Strategies

Legal Domain Focus

Medical Domain Focus

Technical Domain Focus

Project and Assessment

Summary and Next Steps

[language] => en [duration] => 28 [status] => published [changed] => 1715281386 [source_title] => Small Language Models (SLMs) for Domain-Specific Applications [source_language] => en [cert_code] => [weight] => -1002 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slmsdsa ) [slmseeai] => stdClass Object ( [course_code] => slmseeai [hr_nid] => 479667 [title] => Small Language Models (SLMs): Developing Energy-Efficient AI [requirements] =>

Audience

[overview] =>

Small Language Models (SLMs) are efficient alternatives to larger models, offering comparable performance with significantly reduced computational and energy requirements.

This instructor-led, live training (online or onsite) is aimed at advanced-level machine learning engineers and AI researchers who wish to develop energy-efficient AI solutions with small language models that are both powerful and environmentally friendly.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at advanced-level machine learning engineers and AI researchers who wish to develop energy-efficient AI solutions with small language models that are both powerful and environmentally friendly.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Energy-Efficient AI

Compact Model Architectures

Optimization and Compression Techniques

Hardware Considerations for AI

Green Coding Practices

Renewable Energy and AI

Lifecycle Assessment of AI Systems

Policy and Regulation for Sustainable AI

Project and Assessment

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1715307649 [source_title] => Small Language Models (SLMs): Developing Energy-Efficient AI [source_language] => en [cert_code] => [weight] => -1004 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slmseeai ) [slmshai] => stdClass Object ( [course_code] => slmshai [hr_nid] => 479659 [title] => Small Language Models (SLMs) for Human-AI Interactions [requirements] =>

Audience

[overview] =>

Small Language Models (SLMs) are compact yet powerful tools for enabling sophisticated human-AI interactions in various applications, including conversational AI and customer service bots.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists, machine learning and AI researchers who wish to create engaging and efficient AI-powered conversational experiences with small language models.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level data scientists, machine learning and AI researchers who wish to create engaging and efficient AI-powered conversational experiences with small language models.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Conversational AI and Small Language Models (SLMs)

Designing Conversational Flows

Building Customer Service Bots

Training SLMs for Interaction

Evaluating Interaction Quality

Voice-Enabled and Multimodal Interactions

Personalization and Contextual Understanding

Ethical Considerations and Bias Mitigation

Deployment and Scaling

Capstone Project

Final Assessment

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1715283400 [source_title] => Small Language Models (SLMs) for Human-AI Interactions [source_language] => en [cert_code] => [weight] => -1003 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slmshai ) [slmsodai] => stdClass Object ( [course_code] => slmsodai [hr_nid] => 479671 [title] => Small Language Models (SLMs) for On-Device AI [requirements] =>

Audience

[overview] =>

Small Language Models (SLMs) are efficient and versatile AI tools that can be implemented on a variety of devices, from smartphones to IoT devices, enabling intelligent on-device applications.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IT professionals who wish to deploy small language models directly onto devices with limited processing capabilities, opening up possibilities for innovative applications in various sectors.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level IT professionals who wish to deploy small language models directly onto devices with limited processing capabilities, opening up possibilities for innovative applications in various sectors.

By the end of this training, participants will be able to:

[outline] =>

Introduction to On-Device AI

Model Optimization for On-Device Deployment

Platform-Specific AI Tools and Frameworks

Real-Time Inference and Edge Computing

Power Management and Battery Life Considerations

Security and Privacy in On-Device AI

User Experience and Interaction Design

Scalability and Maintenance

Project and Assessment

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1715323768 [source_title] => Small Language Models (SLMs) for On-Device AI [source_language] => en [cert_code] => [weight] => -1005 [excluded_sites] => hitrait [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slmsodai ) [geminiai] => stdClass Object ( [course_code] => geminiai [hr_nid] => 476043 [title] => Introduction to Google Gemini AI [requirements] =>

Audience


 

[overview] =>

Google Gemini AI is a cutting-edge large language model that offers advanced AI capabilities, such as natural language understanding, text generation, and semantic search, enabling developers to create more intuitive and responsive AI-driven applications.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to integrate AI functionalities into their applications using Google Gemini AI.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to integrate AI functionalities into their applications using Google Gemini AI.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AI and Google Gemini

Understanding Large Language Models (LLMs)

Getting Started with Google Gemini

Working with Gemini Models

Practical Applications of Gemini AI

Advanced Features and Customization

Project - Building an AI Code Buddy

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1711952394 [source_title] => Introduction to Google Gemini AI [source_language] => en [cert_code] => [weight] => -1005 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiai ) [geminiaiforcontentcreation] => stdClass Object ( [course_code] => geminiaiforcontentcreation [hr_nid] => 476187 [title] => Google Gemini AI for Content Creation [requirements] =>

Audience

[overview] =>

Google Gemini AI is a transformative tool for content creators, offering capabilities that streamline the creation process of content for various mediums, such as web content, marketing materials, and multimedia projects.

This instructor-led, live training (online or onsite) is aimed at intermediate-level content creators who wish to utilize Google Gemini AI to enhance their content quality and efficiency.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level content creators who wish to utilize Google Gemini AI to enhance their content quality and efficiency.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AI-Powered Content Creation

Setting Up Google Gemini for Content Projects

Automating Content Generation with Gemini AI

Personalizing Content with Gemini AI

SEO Optimization with Gemini AI

Analyzing Content Performance with Gemini AI

Project - Creating a Content Campaign

Conclusion and Future of AI in Content Creation

[language] => en [duration] => 14 [status] => published [changed] => 1711653905 [source_title] => Google Gemini AI for Content Creation [source_language] => en [cert_code] => [weight] => -1007 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiaiforcontentcreation ) [geminiaiforcustomerservice] => stdClass Object ( [course_code] => geminiaiforcustomerservice [hr_nid] => 476047 [title] => Google Gemini AI for Transformative Customer Service [requirements] =>

Audience

[overview] =>

Google Gemini AI is a versatile tool designed to revolutionize customer service interactions by leveraging advanced machine learning algorithms. It enhances real-time communication across various platforms such as live chat, email support, and social media engagement. By automating routine tasks and providing actionable insights from customer data, Google Gemini AI significantly improves the overall customer experience and operational efficiency.

This instructor-led, live training (online or onsite) is aimed at intermediate-level customer service professionals who wish to implement Google Gemini AI in their customer service operations.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level customer service professionals who wish to implement Google Gemini AI in their customer service operations.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AI in Customer Service

Setting Up Google Gemini for Customer Interactions

Automating Customer Support with Gemini AI

Enhancing Customer Engagement

Analyzing Customer Feedback with Gemini AI

Case Studies and Best Practices

Project - Implementing Gemini AI Chatbot

Conclusion and Future Trends

[language] => en [duration] => 14 [status] => published [changed] => 1711648466 [source_title] => Google Gemini AI for Transformative Customer Service [source_language] => en [cert_code] => [weight] => -1006 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiaiforcustomerservice ) [geminiaifordataanalysis] => stdClass Object ( [course_code] => geminiaifordataanalysis [hr_nid] => 476191 [title] => Google Gemini AI for Data Analysis [requirements] =>

Audience

[overview] =>

Google Gemini AI is a cutting-edge tool that provides users with natural language and visual interfaces to enhance data exploration, analysis, visualization, and communication.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level data analysts and business professionals who wish to perform complex data analysis tasks more intuitively across various industries using Google Gemini AI.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level data analysts and business professionals who wish to perform complex data analysis tasks more intuitively across various industries using Google Gemini AI.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Google Gemini AI

Connecting Data Sources

Exploring Data with Gemini AI

Data Analysis and Insights

Data Visualization

Communicating Insights

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1711656398 [source_title] => Google Gemini AI for Data Analysis [source_language] => en [cert_code] => [weight] => -1008 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiaifordataanalysis ) [generativeaillm] => stdClass Object ( [course_code] => generativeaillm [hr_nid] => 463251 [title] => Generative AI with Large Language Models (LLMs) [requirements] =>

Audience

[overview] =>

Generative AI is a type of AI that can create original content such as text, images, music, and code. Large language models (LLMs) are powerful neural networks that can process and generate natural language. 

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to learn how to use generative AI with LLMs for various tasks and domains.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level developers who wish to learn how to use generative AI with LLMs for various tasks and domains.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Generative AI

Transformer Architecture and LLMs

Scaling Laws and Optimization

Training and Fine-Tuning LLMs

Deploying and Using LLMs

Ethics and Future of Generative AI

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1709073362 [source_title] => Generative AI with Large Language Models (LLMs) [source_language] => en [cert_code] => [weight] => -1004 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => generativeaillm ) [llamaindex] => stdClass Object ( [course_code] => llamaindex [hr_nid] => 476587 [title] => LlamaIndex: Enhancing Contextual AI [requirements] =>

Audience

[overview] =>

LlamaIndex is an open-source data framework designed for applications that use Large Language Models (LLMs) and benefit from context augmentation. It's particularly useful for systems known as Retrieval-Augmented Generation (RAG) systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI researchers, machine learning professionals, and data scientists who wish to use LlamaIndex to enhance the capabilities of AI models, making them more accurate and reliable for various applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level AI researchers, machine learning professionals, and data scientists who wish to use LlamaIndex to enhance the capabilities of AI models, making them more accurate and reliable for various applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction to LlamaIndex and Context Augmentation

Setting Up LlamaIndex

Data Indexing and Access

Integrating LlamaIndex with LLMs

Application Scenarios and Case Studies

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1712369760 [source_title] => LlamaIndex: Enhancing Contextual AI [source_language] => en [cert_code] => [weight] => -1009 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => llamaindex ) [llamaindexdev] => stdClass Object ( [course_code] => llamaindexdev [hr_nid] => 476715 [title] => LlamaIndex: Developing LLM Powered Applications [requirements] =>

Audience

[overview] =>

LlamaIndex is a powerful indexing tool designed to enhance the capabilities of Large Language Models (LLMs) by allowing them to retrieve and utilize custom data sets effectively.

This instructor-led, live training (online or onsite) is aimed at beginner-level to advanced-level developers and data scientists who wish to master LlamaIndex for developing innovative LLM-powered applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to advanced-level developers and data scientists who wish to master LlamaIndex for developing innovative LLM-powered applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction to LlamaIndex

LlamaIndex in Action

Advanced LlamaIndex Features

Application Development with LlamaIndex

Deployment and Scaling

Ethical and Practical Considerations

Summary and Next Steps

[language] => en [duration] => 42 [status] => published [changed] => 1712622902 [source_title] => LlamaIndex: Developing LLM Powered Applications [source_language] => en [cert_code] => [weight] => -1010 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => llamaindexdev ) [llm] => stdClass Object ( [course_code] => llm [hr_nid] => 462191 [title] => Introduction to Large Language Models (LLMs) [requirements] =>

Audience

[overview] =>

Large Language Models (LLMs) are deep neural network models that can generate natural language texts based on a given input or context. They are trained on large amounts of text data from various domains and sources, and they can capture the syntactic and semantic patterns of natural language. LLMs have achieved impressive results on various natural language tasks such as text summarization, question answering, text generation, and more.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Understanding LLMs

Getting Started

Working with LLMs

Text Summarization

Question Answering

Text Generation

Integrating LLMs with Other Frameworks and Platforms

Troubleshooting

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1700095996 [source_title] => Introduction to Large Language Models (LLMs) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => llm ) ) [codes] => Array ( [0] => langchain [1] => langchainfun [2] => slms [3] => slmsdsa [4] => slmseeai [5] => slmshai [6] => slmsodai [7] => geminiai [8] => geminiaiforcontentcreation [9] => geminiaiforcustomerservice [10] => geminiaifordataanalysis [11] => generativeaillm [12] => llamaindex [13] => llamaindexdev [14] => llm ) ) [4] => Array ( [regions] => Array ( [ve_1805] => Array ( [tid] => ve_1805 [title] => Caracas [sales_area] => ve_venezuela [venues] => Array ( [ve_10638169] => Array ( [vid] => ve_10638169 [title] => Caracas - Centro Lido [vfdc] => 250.00 [prices] => Array ( [1] => Array ( [remote guaranteed] => 5990 [classroom guaranteed] => 6990 [remote guaranteed per delegate] => 5990 [delegates] => 1 [adp] => 1250 [classroom guaranteed per delegate] => 6990 ) [2] => Array ( [remote guaranteed] => 7240 [classroom guaranteed] => 8540 [remote guaranteed per delegate] => 3620 [delegates] => 2 [adp] => 1250 [classroom guaranteed per delegate] => 4270 ) [3] => Array ( [remote guaranteed] => 8490 [classroom guaranteed] => 10089 [remote guaranteed per delegate] => 2830 [delegates] => 3 [adp] => 1250 [classroom guaranteed per delegate] => 3363 ) [4] => Array ( [remote guaranteed] => 9740 [classroom guaranteed] => 11640 [remote guaranteed per delegate] => 2435 [delegates] => 4 [adp] => 1250 [classroom guaranteed per delegate] => 2910 ) [5] => Array ( [remote guaranteed] => 10990 [classroom guaranteed] => 13190 [remote guaranteed per delegate] => 2198 [delegates] => 5 [adp] => 1250 [classroom guaranteed per delegate] => 2638 ) [6] => Array ( [remote guaranteed] => 12240 [classroom guaranteed] => 14742 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delegate] => 5990 [adp] => 1250 ) [2] => Array ( [remote guaranteed] => 7240 [remote guaranteed per delegate] => 3620 [adp] => 1250 ) [3] => Array ( [remote guaranteed] => 8490 [remote guaranteed per delegate] => 2830 [adp] => 1250 ) [4] => Array ( [remote guaranteed] => 9740 [remote guaranteed per delegate] => 2435 [adp] => 1250 ) [5] => Array ( [remote guaranteed] => 10990 [remote guaranteed per delegate] => 2198 [adp] => 1250 ) [6] => Array ( [remote guaranteed] => 12240 [remote guaranteed per delegate] => 2040 [adp] => 1250 ) [7] => Array ( [remote guaranteed] => 13489 [remote guaranteed per delegate] => 1927 [adp] => 1250 ) [8] => Array ( [remote guaranteed] => 14744 [remote guaranteed per delegate] => 1843 [adp] => 1250 ) [9] => Array ( [remote guaranteed] => 15993 [remote guaranteed per delegate] => 1777 [adp] => 1250 ) [10] => Array ( [remote guaranteed] => 17240 [remote guaranteed per delegate] => 1724 [adp] => 1250 ) ) [currency] => USD ) [5] => Array ( ) [6] => Array ( ) [7] => 0 [8] => 1 [9] => 1 [10] => ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/generativeairobot ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ve/en/cc/generativeairobot Undefined array key "nobleprog_site_production_url" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_site_production_url" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 131 [function] => variable_get [args] => Array ( [0] => nobleprog_site_production_url ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7_block.inc [line] => 44 [function] => islc_get_current_site [args] => Array ( ) ) [3] => Array ( [file] => /apps/nobleprog-website/nptemplates/default.php [line] => 265 [function] => islc7_sites_links_array_v3 [args] => Array ( ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 85 [args] => Array ( [0] => /apps/nobleprog-website/nptemplates/default.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 31 [function] => course_render [args] => Array ( [0] => Array ( [course_code] => generativeairobot [hr_nid] => 479011 [title] => Generative AI in Robotics: Creating Autonomous Solutions [requirements] =>

Audience

[overview] =>

Generative AI is a cutting-edge field of AI that focuses on creating systems that can generate new, complex patterns and behaviors.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level robotics engineers and AI researchers who wish to design and implement autonomous robotic systems using Generative AI techniques.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level to advanced-level robotics engineers and AI researchers who wish to design and implement autonomous robotic systems using Generative AI techniques.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Generative AI in Robotics

Designing AI-Generated Robots

AI in Robotic Perception and Decision-Making

Robotics in Manufacturing and Industry

AI Robotics in Service and Healthcare

Challenges and Future Directions

Capstone Project

Summary and Next Steps

[language] => en [duration] => 28 [status] => published [changed] => 1714523875 [source_title] => Generative AI in Robotics: Creating Autonomous Solutions [source_language] => en [cert_code] => [weight] => -1010 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) ) [1] => Array ( [0] => stdClass Object ( [tid] => 4879 [alias] => generative-ai-training [name] => Generative AI [english_name] => Generative AI [consulting_option] => available ) ) [2] => generativeairobot [3] => Array ( [outlines] => Array ( [langchain] => stdClass Object ( [course_code] => langchain [hr_nid] => 476371 [title] => LangChain: Building AI-Powered Applications [requirements] =>

Audience

[overview] =>

LangChain is an open-source framework designed to facilitate the development of applications using large language models (LLMs).

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers and software engineers who wish to build AI-powered applications using the LangChain framework.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level developers and software engineers who wish to build AI-powered applications using the LangChain framework.

By the end of this training, participants will be able to:

[outline] =>

Introduction to LangChain

Understanding Large Language Models (LLMs)

LangChain Components and Architecture

Integrating LangChain with LLMs

Building Modular Applications

Practical Exercises with LangChain

Advanced LangChain Features

Best Practices and Patterns

Troubleshooting

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1712088649 [source_title] => LangChain: Building AI-Powered Applications [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => langchain ) [langchainfun] => stdClass Object ( [course_code] => langchainfun [hr_nid] => 476375 [title] => LangChain Fundamentals [requirements] =>

Audience

[overview] =>

LangChain is an open-source framework that simplifies the integration of large language models (LLMs) into applications.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers and software engineers who wish to learn the core concepts and architecture of LangChain and gain the practical skills for building AI-powered applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level developers and software engineers who wish to learn the core concepts and architecture of LangChain and gain the practical skills for building AI-powered applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction to LangChain

Setting Up the Environment

Core Concepts of LangChain

Working with Large Language Models (LLMs)

Developing with LangChain

Troubleshooting

Conclusion and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1712091756 [source_title] => LangChain Fundamentals [source_language] => en [cert_code] => [weight] => -1002 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => langchainfun ) [slms] => stdClass Object ( [course_code] => slms [hr_nid] => 479495 [title] => Small Language Models (SLMs): Applications and Innovations [requirements] =>

Audience

[overview] =>

Small Language Models (SLMs) are a cutting-edge subset of AI that enables efficient language processing on devices with limited computational resources.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level data scientists and developers who wish to implement and leverage Small Language Models in various applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level data scientists and developers who wish to implement and leverage Small Language Models in various applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Small Language Models (SLMs)

Technical Foundations

SLMs in Natural Language Processing

Real-world Applications of SLMs

Case Studies

Future Directions

Hands-on Workshops

Capstone Project

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1715280132 [source_title] => Small Language Models (SLMs): Applications and Innovations [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slms ) [slmsdsa] => stdClass Object ( [course_code] => slmsdsa [hr_nid] => 479651 [title] => Small Language Models (SLMs) for Domain-Specific Applications [requirements] =>

Audience

[overview] =>

Small Language Models (SLMs) are a cutting-edge subset of AI that enables efficient language processing on devices with limited computational resources.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists and machine learning engineers who wish to create and apply small language models tailored for specific domains such as legal, medical, and technical fields.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level data scientists and machine learning engineers who wish to create and apply small language models tailored for specific domains such as legal, medical, and technical fields.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Domain-Specific Language Models

Data Curation and Preprocessing

Model Training and Fine-Tuning

Evaluation Metrics and Model Performance

Deployment Strategies

Legal Domain Focus

Medical Domain Focus

Technical Domain Focus

Project and Assessment

Summary and Next Steps

[language] => en [duration] => 28 [status] => published [changed] => 1715281386 [source_title] => Small Language Models (SLMs) for Domain-Specific Applications [source_language] => en [cert_code] => [weight] => -1002 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slmsdsa ) [slmseeai] => stdClass Object ( [course_code] => slmseeai [hr_nid] => 479667 [title] => Small Language Models (SLMs): Developing Energy-Efficient AI [requirements] =>

Audience

[overview] =>

Small Language Models (SLMs) are efficient alternatives to larger models, offering comparable performance with significantly reduced computational and energy requirements.

This instructor-led, live training (online or onsite) is aimed at advanced-level machine learning engineers and AI researchers who wish to develop energy-efficient AI solutions with small language models that are both powerful and environmentally friendly.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at advanced-level machine learning engineers and AI researchers who wish to develop energy-efficient AI solutions with small language models that are both powerful and environmentally friendly.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Energy-Efficient AI

Compact Model Architectures

Optimization and Compression Techniques

Hardware Considerations for AI

Green Coding Practices

Renewable Energy and AI

Lifecycle Assessment of AI Systems

Policy and Regulation for Sustainable AI

Project and Assessment

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1715307649 [source_title] => Small Language Models (SLMs): Developing Energy-Efficient AI [source_language] => en [cert_code] => [weight] => -1004 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slmseeai ) [slmshai] => stdClass Object ( [course_code] => slmshai [hr_nid] => 479659 [title] => Small Language Models (SLMs) for Human-AI Interactions [requirements] =>

Audience

[overview] =>

Small Language Models (SLMs) are compact yet powerful tools for enabling sophisticated human-AI interactions in various applications, including conversational AI and customer service bots.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists, machine learning and AI researchers who wish to create engaging and efficient AI-powered conversational experiences with small language models.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level data scientists, machine learning and AI researchers who wish to create engaging and efficient AI-powered conversational experiences with small language models.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Conversational AI and Small Language Models (SLMs)

Designing Conversational Flows

Building Customer Service Bots

Training SLMs for Interaction

Evaluating Interaction Quality

Voice-Enabled and Multimodal Interactions

Personalization and Contextual Understanding

Ethical Considerations and Bias Mitigation

Deployment and Scaling

Capstone Project

Final Assessment

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1715283400 [source_title] => Small Language Models (SLMs) for Human-AI Interactions [source_language] => en [cert_code] => [weight] => -1003 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slmshai ) [slmsodai] => stdClass Object ( [course_code] => slmsodai [hr_nid] => 479671 [title] => Small Language Models (SLMs) for On-Device AI [requirements] =>

Audience

[overview] =>

Small Language Models (SLMs) are efficient and versatile AI tools that can be implemented on a variety of devices, from smartphones to IoT devices, enabling intelligent on-device applications.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IT professionals who wish to deploy small language models directly onto devices with limited processing capabilities, opening up possibilities for innovative applications in various sectors.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level IT professionals who wish to deploy small language models directly onto devices with limited processing capabilities, opening up possibilities for innovative applications in various sectors.

By the end of this training, participants will be able to:

[outline] =>

Introduction to On-Device AI

Model Optimization for On-Device Deployment

Platform-Specific AI Tools and Frameworks

Real-Time Inference and Edge Computing

Power Management and Battery Life Considerations

Security and Privacy in On-Device AI

User Experience and Interaction Design

Scalability and Maintenance

Project and Assessment

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1715323768 [source_title] => Small Language Models (SLMs) for On-Device AI [source_language] => en [cert_code] => [weight] => -1005 [excluded_sites] => hitrait [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => slmsodai ) [geminiai] => stdClass Object ( [course_code] => geminiai [hr_nid] => 476043 [title] => Introduction to Google Gemini AI [requirements] =>

Audience


 

[overview] =>

Google Gemini AI is a cutting-edge large language model that offers advanced AI capabilities, such as natural language understanding, text generation, and semantic search, enabling developers to create more intuitive and responsive AI-driven applications.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to integrate AI functionalities into their applications using Google Gemini AI.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to integrate AI functionalities into their applications using Google Gemini AI.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AI and Google Gemini

Understanding Large Language Models (LLMs)

Getting Started with Google Gemini

Working with Gemini Models

Practical Applications of Gemini AI

Advanced Features and Customization

Project - Building an AI Code Buddy

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1711952394 [source_title] => Introduction to Google Gemini AI [source_language] => en [cert_code] => [weight] => -1005 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiai ) [geminiaiforcontentcreation] => stdClass Object ( [course_code] => geminiaiforcontentcreation [hr_nid] => 476187 [title] => Google Gemini AI for Content Creation [requirements] =>

Audience

[overview] =>

Google Gemini AI is a transformative tool for content creators, offering capabilities that streamline the creation process of content for various mediums, such as web content, marketing materials, and multimedia projects.

This instructor-led, live training (online or onsite) is aimed at intermediate-level content creators who wish to utilize Google Gemini AI to enhance their content quality and efficiency.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level content creators who wish to utilize Google Gemini AI to enhance their content quality and efficiency.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AI-Powered Content Creation

Setting Up Google Gemini for Content Projects

Automating Content Generation with Gemini AI

Personalizing Content with Gemini AI

SEO Optimization with Gemini AI

Analyzing Content Performance with Gemini AI

Project - Creating a Content Campaign

Conclusion and Future of AI in Content Creation

[language] => en [duration] => 14 [status] => published [changed] => 1711653905 [source_title] => Google Gemini AI for Content Creation [source_language] => en [cert_code] => [weight] => -1007 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiaiforcontentcreation ) [geminiaiforcustomerservice] => stdClass Object ( [course_code] => geminiaiforcustomerservice [hr_nid] => 476047 [title] => Google Gemini AI for Transformative Customer Service [requirements] =>

Audience

[overview] =>

Google Gemini AI is a versatile tool designed to revolutionize customer service interactions by leveraging advanced machine learning algorithms. It enhances real-time communication across various platforms such as live chat, email support, and social media engagement. By automating routine tasks and providing actionable insights from customer data, Google Gemini AI significantly improves the overall customer experience and operational efficiency.

This instructor-led, live training (online or onsite) is aimed at intermediate-level customer service professionals who wish to implement Google Gemini AI in their customer service operations.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level customer service professionals who wish to implement Google Gemini AI in their customer service operations.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AI in Customer Service

Setting Up Google Gemini for Customer Interactions

Automating Customer Support with Gemini AI

Enhancing Customer Engagement

Analyzing Customer Feedback with Gemini AI

Case Studies and Best Practices

Project - Implementing Gemini AI Chatbot

Conclusion and Future Trends

[language] => en [duration] => 14 [status] => published [changed] => 1711648466 [source_title] => Google Gemini AI for Transformative Customer Service [source_language] => en [cert_code] => [weight] => -1006 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiaiforcustomerservice ) [geminiaifordataanalysis] => stdClass Object ( [course_code] => geminiaifordataanalysis [hr_nid] => 476191 [title] => Google Gemini AI for Data Analysis [requirements] =>

Audience

[overview] =>

Google Gemini AI is a cutting-edge tool that provides users with natural language and visual interfaces to enhance data exploration, analysis, visualization, and communication.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level data analysts and business professionals who wish to perform complex data analysis tasks more intuitively across various industries using Google Gemini AI.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level data analysts and business professionals who wish to perform complex data analysis tasks more intuitively across various industries using Google Gemini AI.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Google Gemini AI

Connecting Data Sources

Exploring Data with Gemini AI

Data Analysis and Insights

Data Visualization

Communicating Insights

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1711656398 [source_title] => Google Gemini AI for Data Analysis [source_language] => en [cert_code] => [weight] => -1008 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => geminiaifordataanalysis ) [generativeaillm] => stdClass Object ( [course_code] => generativeaillm [hr_nid] => 463251 [title] => Generative AI with Large Language Models (LLMs) [requirements] =>

Audience

[overview] =>

Generative AI is a type of AI that can create original content such as text, images, music, and code. Large language models (LLMs) are powerful neural networks that can process and generate natural language. 

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to learn how to use generative AI with LLMs for various tasks and domains.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level developers who wish to learn how to use generative AI with LLMs for various tasks and domains.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Generative AI

Transformer Architecture and LLMs

Scaling Laws and Optimization

Training and Fine-Tuning LLMs

Deploying and Using LLMs

Ethics and Future of Generative AI

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1709073362 [source_title] => Generative AI with Large Language Models (LLMs) [source_language] => en [cert_code] => [weight] => -1004 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => generativeaillm ) [llamaindex] => stdClass Object ( [course_code] => llamaindex [hr_nid] => 476587 [title] => LlamaIndex: Enhancing Contextual AI [requirements] =>

Audience

[overview] =>

LlamaIndex is an open-source data framework designed for applications that use Large Language Models (LLMs) and benefit from context augmentation. It's particularly useful for systems known as Retrieval-Augmented Generation (RAG) systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI researchers, machine learning professionals, and data scientists who wish to use LlamaIndex to enhance the capabilities of AI models, making them more accurate and reliable for various applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate-level AI researchers, machine learning professionals, and data scientists who wish to use LlamaIndex to enhance the capabilities of AI models, making them more accurate and reliable for various applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction to LlamaIndex and Context Augmentation

Setting Up LlamaIndex

Data Indexing and Access

Integrating LlamaIndex with LLMs

Application Scenarios and Case Studies

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1712369760 [source_title] => LlamaIndex: Enhancing Contextual AI [source_language] => en [cert_code] => [weight] => -1009 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => llamaindex ) [llamaindexdev] => stdClass Object ( [course_code] => llamaindexdev [hr_nid] => 476715 [title] => LlamaIndex: Developing LLM Powered Applications [requirements] =>

Audience

[overview] =>

LlamaIndex is a powerful indexing tool designed to enhance the capabilities of Large Language Models (LLMs) by allowing them to retrieve and utilize custom data sets effectively.

This instructor-led, live training (online or onsite) is aimed at beginner-level to advanced-level developers and data scientists who wish to master LlamaIndex for developing innovative LLM-powered applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to advanced-level developers and data scientists who wish to master LlamaIndex for developing innovative LLM-powered applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction to LlamaIndex

LlamaIndex in Action

Advanced LlamaIndex Features

Application Development with LlamaIndex

Deployment and Scaling

Ethical and Practical Considerations

Summary and Next Steps

[language] => en [duration] => 42 [status] => published [changed] => 1712622902 [source_title] => LlamaIndex: Developing LLM Powered Applications [source_language] => en [cert_code] => [weight] => -1010 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => llamaindexdev ) [llm] => stdClass Object ( [course_code] => llm [hr_nid] => 462191 [title] => Introduction to Large Language Models (LLMs) [requirements] =>

Audience

[overview] =>

Large Language Models (LLMs) are deep neural network models that can generate natural language texts based on a given input or context. They are trained on large amounts of text data from various domains and sources, and they can capture the syntactic and semantic patterns of natural language. LLMs have achieved impressive results on various natural language tasks such as text summarization, question answering, text generation, and more.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Understanding LLMs

Getting Started

Working with LLMs

Text Summarization

Question Answering

Text Generation

Integrating LLMs with Other Frameworks and Platforms

Troubleshooting

Summary and Next Steps

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