Machine Learning from Scratch - course bundle
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Nine models and methods — gradient descent to decision trees — every one implemented in pure numpy on real NBA data.

Run order:
 1. 70_gradient_descent_from_scratch.py  -  Gradient Descent From Scratch: How Models Actually Learn
      https://sportsdatatutorials.com/tutorials/gradient-descent-from-scratch.html
 2. 77_ridge_regression_from_scratch.py  -  Ridge Regression From Scratch: Taming Collinear Features
      https://sportsdatatutorials.com/tutorials/ridge-regression-from-scratch.html
 3. 71_logistic_regression_from_scratch.py  -  Logistic Regression From Scratch: Predicting a Win, Not a Number
      https://sportsdatatutorials.com/tutorials/logistic-regression-from-scratch.html
 4. 72_train_test_split_confusion_matrix.py  -  Train/Test Split and the Confusion Matrix: Grading a Classifier Honestly
      https://sportsdatatutorials.com/tutorials/train-test-split-confusion-matrix.html
 5. 73_kfold_cross_validation.py  -  K-Fold Cross-Validation: A Score You Can Trust
      https://sportsdatatutorials.com/tutorials/kfold-cross-validation.html
 6. 74_roc_curve_and_auc.py  -  ROC Curves and AUC From Scratch
      https://sportsdatatutorials.com/tutorials/roc-curve-and-auc-from-scratch.html
 7. 75_k_nearest_neighbors.py  -  k-Nearest Neighbors From Scratch
      https://sportsdatatutorials.com/tutorials/k-nearest-neighbors-from-scratch.html
 8. 76_decision_tree_from_scratch.py  -  A Decision Tree From Scratch
      https://sportsdatatutorials.com/tutorials/decision-tree-from-scratch.html
 9. 69_kmeans_clustering_from_scratch.py  -  K-Means Clustering From Scratch: Find Team Archetypes
      https://sportsdatatutorials.com/tutorials/kmeans-clustering-from-scratch.html

Every script runs from this folder as-is:  python <script>.py
The shared helpers and every sample CSV the scripts read are included,
so the whole course works offline.

Course page: https://sportsdatatutorials.com/tracks/ml-from-scratch.html
All datasets documented: https://sportsdatatutorials.com/datasets.html
