1. 1. Python Machine Learning
2. 2. Machine Learning
3. 1. 2.1. Types of Machine Learning algorithms
   2. 2.2. ML - Supervised Learning
   3. 2.3. Regression problem
   4. 2.4. Classification problem
   5. 2.5. ML - Unsupervised Learning
   6. 2.6. Linear regression with sklearn
   7. 2.7. Split data set
   8. 2.8. Food-truck linear regression
   9. 2.9. Basic Classification example
   10. 2.10. Kaggle
   11. 2.11. Kaggle - USA housing listing
   12. 2.12. Kaggle - Iris
4. 3. Machine Learning 2
5. 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. Polynomial Regression
   11. 3.11. Normal Equation
   12. 3.12. Multiple features
   13. 3.13. Logistic regression (for classification)
   14. 3.14. Multi-feature Classification (Iris)
   15. 3.15. Kaggle - Melbourne housing listing
   16. 3.16. Machine Learning Resources
   17. 3.17. Regression Analyzis
   18. 3.18. Classification Analysis
   19. 3.19. Unbiased evaluation of a model
   20. 3.20. Splitting data
   21. 3.21. Model selection and validation
   22. 3.22. K-fold valiadtion
   23. 3.23. Learning Curves
   24. 3.24. Hypermatameter tuning (optimization)
   25. 3.25. The k-Nearest Neighbors (kNN)
   26. 3.26. K-Means Clustering
   27. 3.27. Boston housing prices
   28. 3.28. Decision Tree
   29. 3.29. Random Forrest
   30. 3.30. Resnet 50