Section I: Fundamentals
1. An Introduction to Machine Learning
2. The Beginning: Data Pre-Processing
3. Feature Selection
4. Feature Extraction
5. Model Development
Section II: Supervised Learning
6. Regression
7. K-Nearest Neighbors
8. Classification: Logistic Regression and Naïve Bayes Classifier
9. Neural Network I: The Perceptron
10. Neural Network II: The Multi-Layer Perceptron
11. Support Vector Machines
12. Decision Trees
13. An Introduction to Ensemble Learning
Section III: Unsupervised Learning and Deep Learning
14. Clustering
15. Deep Learning
Appendix 1: Glossary
Appendix 2: Methods/Techniques
Appendix 3: Important Metrics and Formulas
Appendix 4: Visualization- Matplotlib
Answers to Multiple Choice Questions