On this page I present a few online courses which I like and which I have either attended/watched or plan to attend/watch. Also, I present a few websites with good university/college lectures.
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
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
12) Calculus Lectures - OpenStax
13) Introduction to Probability Online Course - Dennis Sun
14) Measure Theory Video Course - The Bright Side of Mathematics
15) Calculus Lectures - LibreTexts - University of California, Davis
16) Mathematical methods for economic theory - Martin Osborne
Mathematical methods for economic theory - notes
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