Course curriculum

  • 1

    Welcome to the Course

    • 1. why machine learning is the future
    • 2. applications of machine learning
    • 3. installing python and anaconda
    • Where to get the Python codes and the course materials
  • 2

    Data Preprocessing

    • 4. importing libraries and dataset
    • 5. deal with missing data
    • 6. categorical data
    • 7. splitting dataset into training and test set
    • 8. feature scaling
    • 9. data preprocessing template
  • 3

    Supervised Learning-Regression

    • 12. Polynomial Regression
    • 13. Support Vector Regression
    • 14. Decision Tree Regression
    • 15. Random Forest Regression
    • 16. Evaluating Regression Models Performance
    • 10. simple linear regression
    • 11. Multiple Linear Regression
  • 4

    Supervised Learning-Classification

    • 17. Logistic Regression
    • 18. K-Nearest Neighbors
    • 19. Support Vector Machine
    • 20. Naive Bayes
    • 21. Decision Tree Classification
    • 22. Random Forest Classification
    • 23. Evaluting Classification Models Performance
  • 5

    Unsupervised Learning

    • 24. K-Means Clustering
    • 25. Hierarchical Clustering
    • 26. Recommender System
  • 6

    Dimensionality Reduction

    • 27. Principle Component Analysis
    • 28. Linear Discriminant Analysis
    • 29. Kernel PCA
  • 7

    Introduction to Deep Learning

    • 30. What is Deep Learning
    • 31. Part 1 - Data Preprocessing
    • 32. Part 2 - Create the ANN
    • 33. Part 3 - Making Predictions and Evalutaing model performance