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Course Outline
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Data preprocessing
- Data Cleaning
- Data integration and transformation
- Data reduction
- Discretization and concept hierarchy generation
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Statistical inference
- Probability distributions, Random variables, Central limit theorem
- Sampling
- Confidence intervals
- Statistical Inference
- Hypothesis testing
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Multivariate linear regression
- Specification
- Subset selection
- Estimation
- Validation
- Prediction
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Classification methods
- Logistic regression
- Linear discriminant analysis
- K-nearest neighbours
- Naive Bayes
- Comparison of Classification methods
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Neural Networks
- Fitting neural networks
- Training neural networks issues
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Decision trees
- Regression trees
- Classification trees
- Trees Versus Linear Models
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Bagging, Random Forests, Boosting
- Bagging
- Random Forests
- Boosting
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Support Vector Machines and Flexible disct
- Maximal Margin classifier
- Support vector classifiers
- Support vector machines
- 2 and more classes SVM’s
- Relationship to logistic regression
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Principal Components Analysis
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Clustering
- K-means clustering
- K-medoids clustering
- Hierarchical clustering
- Density based clustering
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Model Assesment and Selection
- Bias, Variance and Model complexity
- In-sample prediction error
- The Bayesian approach
- Cross-validation
- Bootstrap methods
28 Hours
Testimonials (3)
I was benefit from the guidance and sharing life examples + answering all questions.
Marta Melloch - Amazon Development Center Poland Sp. z o.o.
Course - Data Mining and Analysis
I really enjoyed the all the best.
Halil polat - Amazon Development Center Poland Sp. z o.o.
Course - Data Mining and Analysis
The information given was interesting and the best part was towards the end when we were provided with Data from Durex and worked on Data we are familiar with and perform operations to get results.
