The landscape of data science is vast and ever-evolving. With the myriad of resources available, finding a comprehensive list that encapsulates the essence of this field can be daunting. I have curated a guide that offers a holistic view of data science, from understanding classical machine learning algorithms to Bayesian inference and deep learning.

I have hand-selected free courses, ebooks, interactive webpages and podcasts that have played a role in my data science journey. Pick one or two and dive deep.

🔋 - requires more brain power
🪫 - requires less brain power
💎 - personal favourites
👑 - unique

Understanding Machine Learning Predictions

We start with the foundational resources that introduce classical statistical and machine-learning algorithms

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• Python Data Science Handbook - Jake VanderPlas

• An Introduction to Statistical Learning - Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani - \w Python PDF

• Mathematics for Machine Learning - PDF, video and Jupyter Notebooks for introductions to mathematics required for ML

• Algorithms for Decision Making - 💎 an extensive overview of decision-making algorithms under uncertainty, with mathematical formulations and solutions.

• Understanding Machine Learning: From Theory to Algorithms - Cambridge PDF - Shai Shalev Shwartz and Ben David

• CS50's Introduction to Artificial Intelligence with Python - Harvard School of Engineering and Applied Sciences

• Introduction to Machine Learning - MIT Open Learning Library

• CS229 - Machine Learning - Andrew Ng Stanford University

• Machine Learning Foundations - Classical Notebooks, Udemy|Youtube course covering Maths and Code by Jon Khrohn

• mlcourse.ai - Open Machine Learning Course

Deep + Reinforcement Learning

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The world of neural networks and deep architectures is vast. MIT 6.S191 offers a comprehensive introduction, while resources like Deep Learning for Coders with fastai & PyTorch bridge the gap between theory and real-world applications.

• MIT 6.S191 Introduction to Deep Learning
• Deep Learning - DS-GA 1008 · Spring 2020 · NYU Center For Data Science
• Deep Learning for Coders with fastai & PyTorch - Jeremy Howard & Sylvain Gugger - Practical Deep Learning course
• Deep Reinforcement Learning - CS 285 at UC Berkeley

• 🤗 Huggingface Reinforcement Learning

• mlcourse.ai - Open Machine Learning Course

• Natural Language Processing

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  Seeing Machine Learning Algorithms

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  For visual learners, this category is a goldmine. From Illustrated ML to Distill, these resources visually break down complex ML concepts, making them accessible and engaging.
  

• Illustrated ML - 👑 Aims to simplify the intricate world of Machine Learning with clear illustrations.

• Seeing Theory - A visual introduction to probability and statistics 💎

• MLU-Expl{ai}n - Visual explanations of core machine learning concepts

• Distill - Machine Learning Research Should Be Clear, Dynamic and Vivid

Bayesian Modelling: The White Box Machine Learning

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Bayesian statistics has gained significant traction in the data science community. Resources like Think Bayes and Bayesian Methods for Hackers provide a perfect blend of theory and application. For those seeking depth, Richard McElreath's Statistical Rethinking lectures are a treasure trove.

• Think Bayes - Allen Downey’s classic as Jupyter Book
• Bayesian Modeling and Computation in Python - Martin Osvaldo A, Kumar Ravin; Lao Junpeng, 2021
• Bayesian Methods for Hackers - Probabilistic Programming and Bayesian Inference - DevAuthors
• Statistical Rethinking 2019 - 👑 Course Fall 2017 + Pre-recorded Lectures 2022 - Material 2022 - Richard McElreath's lectures from Leipzig University - PyMC codes
• Bayes Rules! - An Introduction to Applied Bayesian Modeling - Alicia A. Johnson, Miles Q. Ott, and Mine Dogucu - PyMC codes - Jim Albert and Jingchen Hu - GitBook
• Probability and Bayesian Modeling -
• Probabilistic Machine Learning - a book series by Kevin Murphy + GitHub materials
• Statistical Thinking for the 21st Century - Russell A. Poldrack's GitBook
• Bayesian Thinking - A Companion to the Statistics with R Course
• Causal Inference for The Brave and True
• Probability Distribution Explorer - Probability distributions and the stories behind

Extras: Convex Optimization

Optimization is at the core of many algorithms. Boyd and Vandenberghe's Convex Optimization is a staple, and the courses from Stanford provide a deeper understanding.

• Convex Optimization – Boyd and Vandenberghe
• EE364A - Convex Optimization I & II - Stephen P. Boyd - Stanford University

Podcasts

For those on-the-go, podcasts like Learning Bayesian Statistics and Linear Digressions provide insights into data science trends, methodologies, and applications.

• Learning Bayesian Statistics - Up-to-date dialogue on Bayesian inference
• Linear Digressions - Host: Katie Malone & Ben Jaffe - Ended 2020 ML dialogue

This curated list has been my one-stop guide collected when I was a beginner and seasoned professional in data science. Whether you're a visual learner, an avid reader, or someone who learns by doing, there's something here for everyone. Dive in and let the exploration begin!

If you have a resource worth adding, fire it on the comments.

Until then, keep on learning …