Machine-Learning-in-Action

我将自己在阅读《机器学习实战》过程中写的源代码放在这里。每个文件夹里也（可能）附有一点简单的笔记。

进度

1. Ch02 - Classifying with k-Nearest Neighbors
2. Ch03 - Splitting datasets one feature at a time: decision trees
3. Ch04 - Classifying with probability theory: naive Bayes
4. Ch05 - Logistic regression
5. Ch06 - Support vector machines
6. Ch07 - Improving classification with the AdaBoost meta-algorithm
7. Ch08 - Predicting numeric values: regression
8. Ch09 - Tree-based regression
9. Ch10 - Grouping unlabeled items using k-means clustering
10. Ch11 - Association analysis with the Apriori algorithm
11. Ch12 - Efficiently finding frequent itemsets with FP-growth
12. Ch13 - Using principal component analysis to simplify data
13. Ch14 - Simplifying data with the singular value decomposition
14. Ch15 - Big data and MapReduce