PART 1. Introduction
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#MODULE11 #SupervisedLearning

1. Supervised learning

2.Exploratory data analysis

PART 2. Classification
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#MODULE11 #SupervisedLearning

1.The classification challenges

2.k-Nearest Neighbors Fit

3.k-Nearest Neighbors Predict

4.Measuring model performance

5.The digits recognition dataset

6.Train Test Split + Fit Predict Accuracy

7.Overfitting and underfitting

PART 3. Regression
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#MODULE11 #SupervisedLearning

1.Introduction to regression

2.Importing data for supervised learning

3.The basics of linear regression

4.Fit & predict for regression

5.Train test split for regression

6.Cross-validation

7.5-fold cross-validation

8.K-Fold CV comparison