Source code from the book Machine Learning in Action. ipynb format & html format, corrected the errors (along with some errors found by myself), updated according to python 3.X.
Machine Learning in Action.pdf: pdf version of the book
MLiA_SourceCode.zip: Source code from the original author (.py format)
- 02 Classifying with k-Nearest Neighbors [notebook]
- 03 Splitting datasets one feature at a time decision trees [notebook]
- 04 Classifying with probability theory naive Bayes [notebook]
- 05 Logistic regression [notebook]
- 06 Support vector machines [notebook]
- 07 Improving classification with the AdaBoost meta-algorithm [notebook]
- 08 Predicting numeric values regression [notebook]
- 09 Tree-based regression [notebook]
- 10 Grouping unlabeled items using k-means clustering [notebook]
- 11 Association analysis with the Apriori algorithm [notebook]
- 12 Efficiently finding frequent itemsets with FP-growth [notebook]
- 13 Using principal component analysis to simplify data [notebook]
- 14 Simplifying data with the singular value decomposition [notebook]