1. Machine Learning (ML) Overview
2. Machine Learning Environment
3. Machine Learning Concepts
4. Feature Engineering (FE)
5. Logistic Regression
6. Classification: SVM (Supervised Vector Machines)
7. Classification: Decision Trees and Random Forests
8. Classification: Naive Bayes
9. Clustering (K-Means)
10. Principal Component Analysis (PCA)
11. Recommendation (collaborative filtering)
12. Time Permitting: Capstone Project
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