The Ultimate FREE Machine Learning Study Plan

Related tags

Machine Learning ml-study-plan
Overview

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 ressources, feel free to open a pull request :)

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Comments
  • Grokking Algorithms Book's link added at the line 100

    Grokking Algorithms Book's link added at the line 100

    A link to Grokking Algorithms book added at the last of the further resources - It isn't mandatory to know algorithms and data structures but reading this book can help learners to know basic algorithms and these implementations in order to have a better view. Also, this book has an eloquent way of teaching which can help people to understand algorithms easily.

    Sincerely, Majid Ghasemi

    opened by majidghassemi 2
  • Added resources for Python, Deep Learning and others

    Added resources for Python, Deep Learning and others

    I have added the following:

    1. Automate the Boring Stuff with Python Book
    2. Neural Networks series by 3Blue1Brown
    3. An article on beginner level datasets
    4. An article on the life cycle of a data science project
    opened by nirmalya8 2
  • add gap between two sections

    add gap between two sections

    Hey, Patrick. I closed my previous pull request because It showing It has conflicts.

    Thus, I created this pull requested, and In this pull requested, I only add a gap between sections and add a link to a youtube video at top of the image.

    Thanks! Gaurav

    opened by ghost 0
  • Update README.md

    Update README.md

    1. added - Essentials of Statistics by Monica Wahi in Statistics section
    2. corrected - some spelling errors
    3. added - Getting Started with Applied Machine Learning in Further readings section
    4. made - some changes in Futher readings section https:// ---> ABC found - dead link in Further readings section (https://towardsdatascience.com/beginners-guide-to-machine-learning-with-python-b9ff35bc9c51)
    opened by waiyanps 0
Owner
Patrick Loeber (Python Engineer)
I create free content about Python and Machine Learning on YouTube and my website.
Patrick Loeber (Python Engineer)
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