Course Outline


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


  • Python programming experience


  • Data Scientists
  14 Hours


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