Machine Learning 2

  1. Number of features
  2. Linear regression
  3. Cost function
  4. Gradient descent
  5. Matrices
  6. Machine Learning - Multiple features
  7. Feature Scaling
  8. Gradient Descent - Learning Rate
  9. Features
  10. Polynomial Regression
  11. Normal Equation
  12. Multiple features
  13. Logistic regression (for classification)
  14. Multi-feature Classification (Iris)
  15. Kaggle - Melbourne housing listing
  16. Machine Learning Resources
  17. Regression Analyzis
  18. Classification Analysis
  19. Unbiased evaluation of a model
  20. Splitting data
  21. Model selection and validation
  22. K-fold valiadtion
  23. Learning Curves
  24. Hypermatameter tuning (optimization)
  25. The k-Nearest Neighbors (kNN)
  26. K-Means Clustering
  27. Boston housing prices
  28. Decision Tree
  29. Random Forrest
  30. Resnet 50