1. Introduction
  2. Supervised Machine Learning
  3. System Analysis with Machine Learning/Un-Supervised Learning
  4. Feature Engineering
  5. Classification, Clustering, Association Rules, and Regression
  6. Time Series Analysis
  7. Data Cleanup, Characteristics and Feature Selection
  8. Ensemble Model Development
  9. Design with Deep Learning
  10. Design with Multi Layered Perceptron (MLP)
  11. Long Short Term Memory Networks
  12. Autoencoders
  13. Applications of Machine Learning and Deep Learning
  14. Emerging and Future Technologies.