# 2022-Machine-Learning-Specialization

**Repository Path**: TerryPro/2022-Machine-Learning-Specialization

## Basic Information

- **Project Name**: 2022-Machine-Learning-Specialization
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-09-11
- **Last Updated**: 2024-09-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 2022-Machine-Learning-Specialization


吴恩达2022新版机器学习 machine learning specialization  
课程官网：https://www.coursera.org/specializations/machine-learning-introduction  
bilibili:https://www.bilibili.com/video/BV19B4y1W76i  
github:https://github.com/kaieye/2022-Machine-Learning-Specialization  
课程代码及测验内容已更新完毕  
欢迎pull request，无论是补充学习文件还是优化md笔记  
交流群：772590431
## 课程大纲
Machine learning specialization课程共分为三部分  
- 第一部分：Supervised Machine Learning: Regression and Classification  
- 第二部分：Advanced Learning Algorithms  
- 第三部分：Unsupervised Learning: Recommenders, Reinforcement Learning   

课程slides地址：https://www.deeplearning.ai/courses/machine-learning-specialization/?utm_campaign=mls-video-series&utm_medium=video&utm_source=youtube#course-slides

Machine Learning Specialization by Andrew Ng in 2022  
Course website:https://www.coursera.org/specializations/machine-learning-introduction    
bilibili:https://www.bilibili.com/video/BV19B4y1W76i    
github:https://github.com/kaieye/2022-Machine-Learning-Specialization  
Course code and test content have been updated  
Welcome to pull requests, whether it is to supplement learning files or markdown notes
## Course Outline
Machine learning specialization is divided into 3 parts 
- Part 1：Supervised Machine Learning: Regression and Classification  
- Part 2：Advanced Learning Algorithms  
- Part 3：Unsupervised Learning: Recommenders, Reinforcement Learning 

The second part is currently Uploaded  
slides:https://www.deeplearning.ai/courses/machine-learning-specialization/?utm_campaign=mls-video-series&utm_medium=video&utm_source=youtube#course-slides

## 环境配置

按照操作系统类型安装python（官方使用的环境为3.7.6），安装方式各异。安装成功后在cmd/bash中定位到该文件夹，并使用如下命令安装依赖。

```text
pip install -r requirements.txt
```

mac/linux用户需将pip切换成pip3