Course curriculum

    1. 1. why machine learning is the future

    2. 2. applications of machine learning

    3. 3. installing python and anaconda

    4. Where to get the Python codes and the course materials

    1. 4. importing libraries and dataset

    2. 5. deal with missing data

    3. 6. categorical data

    4. 7. splitting dataset into training and test set

    5. 8. feature scaling

    6. 9. data preprocessing template

    1. 12. Polynomial Regression

    2. 13. Support Vector Regression

    3. 14. Decision Tree Regression

    4. 15. Random Forest Regression

    5. 16. Evaluating Regression Models Performance

    6. 10. simple linear regression

    7. 11. Multiple Linear Regression

    1. 17. Logistic Regression

    2. 18. K-Nearest Neighbors

    3. 19. Support Vector Machine

    4. 20. Naive Bayes

    5. 21. Decision Tree Classification

    6. 22. Random Forest Classification

    7. 23. Evaluting Classification Models Performance

    1. 24. K-Means Clustering

    2. 25. Hierarchical Clustering

    3. 26. Recommender System

    1. 27. Principle Component Analysis

    2. 28. Linear Discriminant Analysis

    3. 29. Kernel PCA

About this course

  • $199.99
  • 34 lessons
  • 4 hours of video content