This is my not-exhaustive collection of the best resources on Machine learning I have encountered so far. I will try to update this constantly. Suggestions are very much appreciated! But first.. since awesome resources require an equally-awesome image, here's the largest NASA Hubble Space Telescope image ever assembled as 2020, representing a bird's-eye view of a portion of the Andromeda galaxy (M31)

Portion of the Andromeda galaxy (M31). We are looking at over 100 million stars in a 61,000-light-year-long stretch of the galaxy's disk, 2 million light-years away from us. (source)

• RL Course by David Silver: course on RL by the lead of DeepMind RL group.
• Dissecting Reinforcement Learning - Series (M. Patacchiola's blog): introductory lectures on Reinforcement Learning. Code is provided and explained for every step. Very interesting overview on the intersection between RL and neuroscience.
• Henry AI RL series: series through the Introduction to Reinforcement Learning book (Sutton and Barto)

• Bayesian methods - Series (M. Patacchiola's blog): introduction on hierarchical Bayesian methods (MLE, MAP, Full bayesian, GMM, etc..).
• Think Bayes: great book for an applied perspective on Bayesian statistics.

• There and Back Again: A Tale of Slopes and Expectations: NeurIPS tutorial on integration, calculus and stochastic sampling useful for variational inference, parameterised inference, Monte Carlo methods, etc..
• Mathematics for Machine learning: book by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong.
• Statistics 110: Lecture videos by Joe Blitzstein (Harvard)

• Probably Overthinking It: bi-monthly(ish) blog on Data science, statistics, Bayesian inference and causality by Allen Downey. The author also wrote the books Think Bayes, or Think Python, also available for free!
• LessWrong: posts from different authors, mainly on causality, Bayesian statistics, rationality, philosophy and Effective Altruism
• Distill.pub: Awesome articles on Machine Learning and stunning visualizations
• Michael Nielsen page: Posts, lectures, projects on AI, Physics, science

• LaTeX Math Symbols
• Writing in the sciences: The best course I have found on scientific writing, as well as personal essays and proposals. It is a Coursera course offered by Standford, hence free to audit.

• Machine Learning Tokyo Meetup group: Weekly meetups on Machine learning, mathematics, awesome events, etc..
• Henry AI Lab Weekly AI: a ~35 minutes weekly update on AI every Monday. The author also skims over new papers or speak about cool talks held during the past week.
• Sentdex's python programming: Probably the best and most popular collection of videos for python programming you can find online. Also, it's completely free. The courses cover python programming for Machine learning, Data analysis, cool Deep Learning projects (e.g. Self-driving cars on GTA 5), among others.
• ML various lectures: Online Machine Learning lecture notes