patrickloeber/ml-study-plan is ranked 8,050 among these 8 peers; seungeunrho/minimalRL is ranked 8,045.

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: 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. 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. 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. Prerequisites [ ] Linear Algebra and Multivariate Calculus [ ] Statistics [ ] Python 1. Basics

Machine Learning [ ] Coursera Free Course by Andrew Ng [ ] Machine Learning Stanford Full Course on YouTube [ ] Udacity - Introduction to Machine Learning [ ] Machine Learning From Scratch - Playlist on YouTube (Python Engineer) [ ] Books (Optional and not free, but I recommend at least the first one): 2. Deep Learning [ ] Stanford Lecture - Convolutional Neural Networks for Visual Recognition [ ] Learn PyTorch (or Tensorflow) [ ] fast. ai - Free Courses Optional: [ ] Stanford Lecture - Natural Language Processing with Deep Learning [ ] Stanford Lecture- Reinforcement Learning 3. Competitions and Own Projects [ ] Kaggle

[ ] 8 Fun Machine Learning Projects For Beginners 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, . Further readings The cold start problem: how to break into machine learning (Towardsdatascience) How to Start Learning Machine Learning? (GeekforGeeks) How to get started in machine learning - best books and sites

for machine learning (YouTube) How you can get a world-class machine learning education for free (Elite Data Science) Get started with AI and machine learning in 3 months (Aleksa Gordić) One year of deep learning (Fast. ai) Getting Started with Applied Machine Learning (Machine Learning Mastery) GitHub: Further resources added by the community Contributions are welcome! If you can recommend any other resources, feel free to open a pull request :) [ ] Book: Automate The Boring Stuff with Python (Till Chapter

6 for Python Basics, the remaining chapters include the applications of Python) [ ] Book: Python Crash Course by Erric Matthes [ ] Book