Table of Contents

  1. Introduction to Machine Learning
  2. Linear Regression
  3. Classification Using Logistic Regression
  4. Overfitting and Regularization
  5. Feasibility of Learning
  6. Support Vector Machine
  7. Neural Network
  8. Decision Trees
  9. Unsupervised Learning
  10. Theory of Generalization
  11. Bias and Fairness in ML