Mastering Python Machine Learning FROM SCRATCH
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1
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1. why machine learning is the future
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2. applications of machine learning
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3. installing python and anaconda
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Where to get the Python codes and the course materials
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2
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4. importing libraries and dataset
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5. deal with missing data
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6. categorical data
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7. splitting dataset into training and test set
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8. feature scaling
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9. data preprocessing template
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3
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12. Polynomial Regression
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13. Support Vector Regression
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14. Decision Tree Regression
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15. Random Forest Regression
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16. Evaluating Regression Models Performance
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10. simple linear regression
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11. Multiple Linear Regression
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4
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17. Logistic Regression
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18. K-Nearest Neighbors
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19. Support Vector Machine
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20. Naive Bayes
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21. Decision Tree Classification
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22. Random Forest Classification
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23. Evaluting Classification Models Performance
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5
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24. K-Means Clustering
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25. Hierarchical Clustering
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26. Recommender System
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6
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27. Principle Component Analysis
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28. Linear Discriminant Analysis
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29. Kernel PCA
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7
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30. What is Deep Learning
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31. Part 1 - Data Preprocessing
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32. Part 2 - Create the ANN
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33. Part 3 - Making Predictions and Evalutaing model performance