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python-engineer/ml-study-plan: The Ultimate FREE Machine Learning Study Plan

原作者: [db:作者] 来自: 网络 收藏 邀请

开源软件名称(OpenSource Name):

python-engineer/ml-study-plan

开源软件地址(OpenSource Url):

https://github.com/python-engineer/ml-study-plan

开源编程语言(OpenSource Language):


开源软件介绍(OpenSource Introduction):

The Ultimate FREE Machine Learning Study Plan

A complete study plan to become a Machine Learning Engineer with links to all FREE resources. If you finish the list you will be equipped with enough theoretical and practical experience to get started in the industry! I tried to limit the resources to a minimum, but some courses are extensive.

Watch the video on YouTube for instructions:
Alt text
https://www.youtube.com/watch?v=dYvt3vSJaQA

#### IMPORTANT: - This list is not sponsored by any of the mentioned links! I did a lot of the courses myself and can highly recommend them! - This list takes a lot of time and effort to finish if you want to do it properly! The list does not look that long, but don't underestimate it.

How to use the Plan:

  • For theory lectures: Follow along, take notes, and repeat the notes afterwards.
  • For practical lectures/courses: Follow along, take notes. If they provide exercises, do them!!! Do not just google the answer, but try to solve it yourself first!
  • For coding tutorials: Code along, and after the video try to code it on your own again.
  • Step 3 is critical! Your theoretical knowledge is worthless if you don't know how to apply it to real world problems! Do as many personal projects and competitions as you can! You don't have to wait with step 3 until you finished the other parts, I recommend starting with a side project or kaggle competition after you finished part 1.1 (Andrew Ng's course).

The Plan

0. Prerequisites

1. Basics Machine Learning

2. Deep Learning

Optional:

3. Competitions and Own Projects

4. Prep for Interviews

Next Level

  • Make your own projects to show what you have learned.
  • Reproduce paper and implement the algorithms.
  • Write a blog to explain what you have learned.
  • Contribute to ML/DL related open source projects (sklearn, pytorch, fastai, ...).
  • Get into Kaggle competitions.

Further readings

GitHub:

Further resources added by the community

Contributions are welcome! If you can recommend any other resources, feel free to open a pull request :)




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