Learning scikit learn - An Introduction to Machine Learning in Python
summary
Hands-on introduction to scikit-learn with runnable notebooks and a GitHub repo, showing a simple linear regression predicting brain weight from head size, logistic regression and k‑NN on the Iris dataset, and how scaling, label encoding, and train/test split (including stratified split) affect results. Examples report concrete numbers (R² for regression, ~80% test accuracy for one run), demonstrate pipelines, show how hyperparameters and random seed change performance, and warn which transforms must be fit only on training data.
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chapters (19)
- 0:00Introduction to scikit-learn
- 1:46Machine learning benefits
- 3:31Types of machine learning
- 6:47Linear regression intro
- 16:16Setting up linear regression
- 22:14Splitting and fitting data
- 31:15Evaluating regression model
- 34:15scikit-learn API overview
- 35:19Iris dataset intro
- 44:49Iris dataset in code
- 50:53Stratified train-test split
- 54:35Logistic regression multi-class
- 59:55Visualizing classification
- 1:02:23Handling categorical features
- 1:08:27Feature normalization
- 1:14:08Building pipelines
- 1:18:46Cross-validation evaluation
- 1:21:48Variance in estimates
- 1:24:42Further resources
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