Machine-Learning-in-Action

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Machine-Learning-in-Action

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🍦Practices & Code from Machine-Learning-in-Action.

Machine-Learning-in-Action 최신버젼 다운로드

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# Machine-Learning-in-Action
Practices & Code from Machine-Learning-in-Action.

《机器学习实战》的笔记与代码。

## schedule
### PART 1 CLASSIFICATION
1. [x] chapter01 - Machine learning basics (机器学习基础)
2. [x] chapter02 - Classifying with k-Nearest Neighbors (k-近邻)
3. [x] chapter03 - Splitting datasets one feature at a time: decision trees (决策树)
4. [x] chapter04 - Classifying with probability theory: naive Bayes (朴素贝叶斯)
5. [x] chapter05 - Logistic regression (Logistic回归)
6. [x] chapter06 - Support vector machines (支持向量机)
7. [x] chapter07 - Improving classification with the AdaBoost meta-algorithm (利用AdaBoost元算法提高分类性能)

### PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION
8. [x] chapter08 - Predicting numeric values: regression (预测数值型数据：回归)
9. [x] chapter09 - Tree-based regression (树回归)

### PART 3 UNSUPERVISED LEARNING
10. [x] chapter10 - Grouping unlabeled items using k-means clustering (利用K-均值聚类算法对未标注数据分组)
11. [x] chapter11 - Association analysis with the Apriori algorithm (使用Apriori算法进行关联分析)
12. [x] chapter12 - Efficiently finding frequent itemsets with FP-growth (使用FP-growth算法来高效发现频繁项集)

### PART 4 ADDITIONAL TOOLS
13. [x] chapter13 - Using principal component analysis to simplify data (利用PCA来简化数据)
14. [x] chapter14 - Simplifying data with the singular value decomposition (利用SVD简化数据)
15. [x] chapter15 - Big data and MapReduce (大数据与MapReduce)

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