mirror of
https://github.com/python-engineer/ml-study-plan
synced 2024-11-23 11:04:57 +00:00
6.1 KiB
6.1 KiB
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.
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!
The Plan
0. 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
- Machine Learning From Scratch - Playlist on YouTube (Python Engineer)
- Books (Optional):
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
- Datasets (develop own projects)
- Competitions (start with Getting started section)
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
- https://towardsdatascience.com/the-cold-start-problem-how-to-break-into-machine-learning-732ee9fedf1d
- https://www.geeksforgeeks.org/how-to-start-learning-machine-learning/
- https://www.youtube.com/watch?v=itzmu0l93wM
- https://elitedatascience.com/learn-machine-learning#step-0
- https://medium.com/@gordicaleksa/get-started-with-ai-and-machine-learning-in-3-months-5236d5e0f230
- https://towardsdatascience.com/beginners-guide-to-machine-learning-with-python-b9ff35bc9c51
- https://www.fast.ai/2019/01/02/one-year-of-deep-learning/
GitHub: