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4.9 KiB
4.9 KiB
The Ultimate FREE Machine Learning Study 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: