1. 1. Python Machine Learning
2. 2. Machine Learning (ML)
   1. 2.1. Types of Machine Learning algorithms
   2. 2.2. Supervised Learning
   3. 2.3. Regression problem
   4. 2.4. Classification problem
   5. 2.5. Unsupervised Learning
   6. 2.6. Clustering vs classification
   7. 2.7. Reinforcement Learning (RL)
   8. 2.8. Linear regression with sklearn
   9. 2.9. Split data set
   10. 2.10. Polynomial Regression
   11. 2.11. Food-truck linear regression
   12. 2.12. Basic Classification example
   13. 2.13. California Housing prices
   14. 2.14. Kaggle
   15. 2.15. Kaggle - USA housing listing
   16. 2.16. Kaggle - Iris
   17. 2.17. Data Preprocessing
3. 3. Machine Learning 2
   1. 3.1. Number of features
   2. 3.2. Linear regression
   3. 3.3. Cost function
   4. 3.4. Gradient descent
   5. 3.5. Matrices
   6. 3.6. Machine Learning - Multiple features
   7. 3.7. Feature Scaling
   8. 3.8. Gradient Descent - Learning Rate
   9. 3.9. Features
   10. 3.10. Normal Equation
   11. 3.11. Multiple features
   12. 3.12. Logistic regression (for classification)
   13. 3.13. Multi-feature Classification (Iris)
   14. 3.14. Kaggle - Melbourne housing listing
   15. 3.15. Machine Learning Resources
   16. 3.16. Regression Analyzis
   17. 3.17. Classification Analysis
   18. 3.18. Unbiased evaluation of a model
   19. 3.19. Splitting data
   20. 3.20. Model selection and validation
   21. 3.21. K-fold valiadtion
   22. 3.22. Learning Curves
   23. 3.23. Hypermatameter tuning (optimization)
   24. 3.24. The k-Nearest Neighbors (kNN)
   25. 3.25. K-Means Clustering
   26. 3.26. Boston housing prices
   27. 3.27. Decision Tree
   28. 3.28. Random Forrest
   29. 3.29. Resnet 50