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Courses

On this page I present a few online courses which I like and which I have either attended/watched or plan to attend/watch.

1) MIT - Probabilistic Systems and Applied Probability

Very good introductory course to probability and statistics. For me it was a bit hard since the language is somewhat loose, certain things are not really rigorously introduced, and I (unlike most people) prefer a more rigorous presentation. But there is a good reason for this non-rigorous style. The audience is diverse, not all attendees have the same math background. Also the textbook is rigorous enough (for an introductory book). Overall it's a great course and I learned so much from it.

2) MIT - Fundamentals of Statistics / Statistics for Applications

3) MIT - Introduction to Computational Thinking and Data Science

4) MIT - Artificial Intelligence

5) MIT - Introduction to Deep Learning

6) Carnegie Mellon - Machine Learning Course - Tom Mitchell

7) Stanford / Coursera - Machine Learning Specialization - Andrew Ng

This specialization consists of 3 courses:

  • Supervised Machine Learning: Regression and Classification
  • Advanced Learning Algorithms
  • Unsupervised Learning, Recommenders, Reinforcement Learning
It's Python based now (in 2022 it was updated to be Python-based).

8) Udemy - Machine Learning A-Z

9) Udemy - Python for DS and ML

10) 100 Lectures on ML - Mark Schmidt

11) Calculus Lectures - Lamar University ( Calculus ICalculus IICalculus III )


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