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Course Outline
Introduction
What is AI
- Computational Psychology
- Computational Philosophy
Machine Learning
- Computational learning theory
- Computer algorithms for computational experience
Deep Learning
- Artificial neural networks
- Deep learning vs. machine learning
Preparing the Development Environment
- Setting up Python libraries and Apache Spark
Recommendation Systems
- Building a recommender engine frameworks
- Testing and evaluating algorithms
Collabrative Filtering
- Working with user-based and content-based filtering
- Working with neighbor-based filtering
- Using RBMs
Matrix Factorization
- Using and extending PCA
- Running and improving SVD
- Working with Keras and deep learning neural networks
Scaling with Spark
- Using RDDs and dataframes
- Setting up clusters on AWS / EC2
- Scaling Amazon DSSTNE and SageMaker
Summary and Conclusion
Requirements
- Python programming experience
Audience
- Data Scientists
14 Hours