- Concrete Mathematics: A Foundation for Computer Science (great generic initial approach)
- Introduction to Linear Algebra, Fifth Edition (2016) (more formal approach)
- Calculus Made Easy - Silvanus P. Thompson
- A Programmer's Introduction to Mathematics (good introduction to several topics, but I personally see too little relation with a programming approach and mindset)
- Linear Algebra Review and Reference - cs229 Stanford TOCHECK
- Numerical Linear Algebra TOCHECK
- Numerical Methods TOCHECK
- Linear Algebra Done Right (Beyond Euclidean spaces and matrices, focusing on abstract vector spaces and linear maps. Pure textbook style and mathematical formalization, very little practical examples and intuitive explanations)
- Practical Linear Algebra: A Geometry Toolbox (linear algebra in a geometric and algorithmic way, through multiple practical and intuitive examples)
- What Is Mathematics? An Elementary Approach to Ideas and Methods TOCHECK
- Basic Mathematics TOCHECK
- Interpolation in Conformal Geometric Algebra TOCHECK
- Think Stats 2e - Allen B. Downey (Python, good intuitive explanation of basic concepts, often from a different POV compared to other more technical books about statistics)
- Think Bayes (Python, many good concrete examples that can be easily implemented and solved from scratch, greatly helps to absorb Bayes's Theorem and related concepts at a more intuitive level. A bit of interdependency between chapters with code written/presented in previous ones)
- OpenIntro Statistics 3rd Edition (exercises and inline questions with solutions)
- Review of Probability Theory - cs229 Stanford
- Probability Theory: The Logic of Science (very technical and detailed. Probably good if you want to properly refine your knowledge, otherwise previous books provide more approachable and standard content)
- An Introduction to Statistical Learning (R)
- The Elements of Statistical Learning 2 (highly technical)
- Bayesian Data AnalysisThird edition TOCHECK
- Time Series Analysis - Hamilton
- Introductory Time Series with R (Use R!) - Cowpertwait and Metcalfe
- Time Series Analysis and Its Applications: With R Examples - Shumway and Stoffer
- Introduction to Time Series and Forecasting - Brockwell and Davis
- Machine Learning - Tom Mitchell - 1997 (A must #1)
- Pattern Recognition and Machine Learning by Christopher M. Bishop (A must #2)
- Applied Predictive Modeling TOREAD
- Model-Based Machine Learning TOCHECK
- Python Machine Learning - Sebastian Raschka TOREAD
- Hands-On Machine Learning with Scikit-Learn and TensorFlow TOCHECK
- The Hundred-Page Machine Learning Book (very well written, with extensive coverage of relevant topics while being succinct and intuitive. A bit less organized in the last chapters.)
- Machine Learning Engineering by Andriy Burkov TOCHECK
- Probabilistic Machine Learning: An Introduction TOREAD
- Patterns, predictions, and actions: A story about machine learning TOREAD
- deeplearningbook (highly technical, also overview and details about many prerequisites to machine learning, like linear algebra and probability)
- neuralnetworksanddeeplearning - Michael Nielsen (online) (great intuitive look into neural-networks properties and deep-learning models working mechanisms)
- Deep Learning with Python - François Chollet (great intuitive description of the fundamentals of deep learning, both in term of methods and components. Relevant Keras code and references for each section)
- Generative Deep Learning (good high-level coverage of generative models, supported by code examples in Tensorflow 2.0)
- Dive into Deep Learning - An interactive deep learning book TOCHECK
- Deep Learning with PyTorch see also Sebastian Raschka review TOCHECK
- The Mathematical Engineering of Deep Learning TOCHECK
- Python Cookbook, 3rd Edition by David Beazley; Brian K. Jone (great collection of advanced recipes, tips and techniques)
- The Pragmatic Programmer: From Journeyman to Master (fantastic overview of best practices and mindset to make life as a programmer as easy, fruitful and pain-free as possible)
- Practical Python Programming (Online)
- 3blue1brown (Video Series) (Pure math entertainment. Stunning visuals and explanations for many concepts, from linear algebra to physics)
- Advanced Machine-Learning Courses
- AtHomeWithAI - DeepMind Curated Resource List
- AI Curriculum
- End-to-End Machine Learning Library a rich list of resources and links for multiple DS related topics
- fastai - courses, books, library
- Computational Linear Algebra for Coders (Jupyter, actual implementation + optimization considerations for many major statistical procedures like PCA, SVD, etc.)
- Statistics @ Udacity
- Computational Probability and Inference @ edX (Python based) TODO
- Machine Learning by Andrew Ng @ Coursera (de facto standard obligatory introductory course)
- Machine Learning by Georgia Tech
- Neural Networks for Machine Learning @ Coursera TODO
- Deep Learning A-Z™: Hands-On Artificial Neural Networks TODO
- Machine Learning For Coders @ Fast.ai TODO
- AWS Machine Learning Course TODO
- TensorFlow Developer Certificate program TOCHECK
- CS231n Convolutional Neural Networks for Visual Recognition
- Practical Deep Learning For Coders, Part 1 (entertaining, great view of SOA results, notebooks and great tips on code)
- Practical Deep Learning For Coders, Part 2 TODO
- Deep Learning by Google @ Udacity
- Creative Applications of Deep Learning with Tensorflow @ Kadenze (good interactive examples and exposition, some specific topics not generally found in other courses, otherwise usual CNN and RNN pass)
- Creative Applications of Deep Learning with TensorFlow II
- Deep Learning by Andrew Ng @ Coursera TODO
- TensorFlow in Practice Specialization @ Coursera TODO
- Standford Deep Generative Models CS236 - Fall 2019
- GANs in computer vision - Introduction to generative learning
- Berkeley Deep Unsupervised Learning Spring 2020
- Generative Adversarial Networks (GANs) @ Coursera TODO
- Deep Learning for Computer Vision - Michigan Online TOCHECK
- Introduction to Computer Vision @ Udacity (detailed and entertaining presentation of the fundamentals of computer vision)
- Hadoop Platform and Application Framework @ Coursera
- Intro to Hadoop and MapReduce @ Udacity
- Machine Learning With Big Data @ Coursera (brief generic intro to machine learning, practical examples for Spark and KNIME)
- Spark Fundamentals 1 (poorly taught, monotonous reading of slides, non relevant quizzes)
- Generative Art and Computational Creativity @ Kadenze (clear overview and exploration of relevant artistic artifacts for different major topic: chaos theory, rule-based system, grammars, markov chains, agents, artificial-life. Coverage of both visual and audio media. Assignments in MAX)
- Interactive 3D Graphics @ Udacity (introductions to many fundamentals of 3D Graphics while playing around with concrete examples and code in three.js)
- Introduction to Programming for the Visual Arts @ Kadenze TODO
- Procedural Modelling @ edx TOCHECK