1. Introduction to Machine Learning
  2. Statistical Analysis
  3. Linear Regression 
  4. Logistic Regression 
  5. Decision Trees
  6. Random Forest
  7. Rule-Based Classifiers
  8. Naïve Bayesian Classifier
  9. K-Nearest Neighbors Classifiers
  10. Support Vector Machine
  11. K-Means Clustering
  12. Dimensionality Reduction
  13. Association Rules Mining and FP Growth
  14. Reinforcement Learning
  15. Applications of ML Algorithms
  16. Applications of Deep Learning
  17. Advance Topics and Future Directions