# Machine-Learning-is-Fun

**Repository Path**: alphakappa/Machine-Learning-is-Fun

## Basic Information

- **Project Name**: Machine-Learning-is-Fun
- **Description**: 我学习机器学习的一些资料和笔记，分主题进行了整理。
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-04-11
- **Last Updated**: 2021-04-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 李威的机器学习读书笔记目录

说明：文章链接里图片、公式没有正确显示的话，可以在简书里查看完整的图片和公式。

## 李航《统计学习方法》(第 2 版)读书笔记

| 文章链接                                                     | 简书                                   |
| ------------------------------------------------------------ | -------------------------------------- |
| [《统计学习方法》第 2 章“感知机”学习笔记](https://www.liwei.party/2019/02/17/machine-learning/perceptron/) | https://www.jianshu.com/p/db7f5e841a56 |
| [《统计学习方法》第 3 章“k 近邻法”学习笔记](https://www.liwei.party/2019/02/17/machine-learning/k-nearest-neighbors/) | https://www.jianshu.com/p/11d0ee7d9e5f |
| [《统计学习方法》第 4 章“朴素贝叶斯法”学习笔记](https://www.liwei.party/2019/02/21/machine-learning/naive-bayes/) | https://www.jianshu.com/p/9a12fe2957db |
| [《统计学习方法》第 5 章“决策树”学习笔记](https://www.liwei.party/2018/11/26/machine-learning/decision-tree/) | https://www.jianshu.com/p/8cdd65cfb121 |
| [《统计学习方法》第 6 章“逻辑回归”学习笔记](https://www.liwei.party/2018/11/27/machine-learning/logistic-regression/) | https://www.jianshu.com/p/c64fba1f2fd2 |
| [《统计学习方法》第 6 章“最大熵模型”学习笔记](https://www.liwei.party/2018/11/28/machine-learning/principle-of-maximum-entropy/) | https://www.jianshu.com/p/350887b5b919 |
| [《统计学习方法》第 7 章“支持向量机”学习笔记](https://www.liwei.party/2018/11/29/machine-learning/support-vector-machines/) | https://www.jianshu.com/p/67fc312373de |
| [《统计学习方法》第 8 章“提升方法”学习笔记](https://www.liwei.party/2018/11/30/machine-learning/boosting/) | https://www.jianshu.com/p/fd46d8c78e29 |
| [《统计学习方法》第 9 章“EM 算法及其推广”学习笔记](https://www.liwei.party/2018/12/01/machine-learning/expectation-maximization-algorithm/) | https://www.jianshu.com/p/6c8274bbb9b9 |
| [《统计学习方法》第 10 章“隐马尔科夫模型”学习笔记](https://www.liwei.party/2018/12/02/machine-learning/hidden-markov-model/) |                                        |
| [《统计学习方法》第 11 章“条件随机场”学习笔记](https://www.liwei.party/2018/12/03/machine-learning/conditional-random-field/) |                                        |

## 其它机器学习文章

| 文章链接                                                     | 简书 |
| ------------------------------------------------------------ | ---- |
| [二分类问题常见的评价指标](https://www.liwei.party/2019/02/25/machine-learning/metric/) |      |
| [K-means 算法](https://www.liwei.party/2019/02/23/machine-learning/k-means/) |      |
| [梯度下降法](https://www.liwei.party/2019/02/23/machine-learning/gradient-descent/) |      |
| [线性回归](https://www.liwei.party/2019/02/23/machine-learning/linear-regression/) |      |
| [使用 NumPy 提供的特征分解与 SVD 分解实现 PCA](https://www.liwei.party/2019/02/21/machine-learning/do-pca-with-numpy/) |      |
| [使用 apriori 算法分析春晚主持人名单](https://www.liwei.party/2019/02/22/machine-learning/analyze-cctv-new-year-s-gala-presenter-with-apriori/) |      |
| [朴素贝叶斯算法应用：垃圾短信分类](https://www.liwei.party/2019/02/21/machine-learning/naive-bayes-for-spam-classification/) |      |
| [朴素贝叶斯算法应用：文档分类](https://www.liwei.party/2019/02/21/machine-learning/naive-bayes-for-document-classification/) |      |
| [关联分析之 Apriori 算法](https://www.liwei.party/2019/02/18/machine-learning/apriori/) |      |
| [SVD 奇异值分解理论推导](https://www.liwei.party/2019/02/18/machine-learning/svd/) |      |
| [LDA 线性判别分析公式推导](https://www.liwei.party/2019/02/18/machine-learning/lda/) |      |
| [信息熵的范围](https://www.liwei.party/2019/02/18/machine-learning/entropy/) |      |
| [白话“主成分分析” 1 ：主成分分析用于降维的思想](https://www.liwei.party/2019/02/17/machine-learning/principal-component-analysis-1/) |      |
| [白话”主成分分析“ 2 ：通过主成分分析复习“线性代数”](https://www.liwei.party/2019/02/17/machine-learning/principal-component-analysis-2/) |      |
| [白话“卡方检验”](https://www.liwei.party/2019/02/11/machine-learning/chi-square-test/) |      |