- Broad not narrow focus (i.e. not just deep learning)
- Emphasis on fundamentals: theory over technology
- Present a clear, optimized path
- Assume strong software engineering foundation
- A Mind For Numbers: How to Excel at Math and Science by Barbara Oakley
- Concepts of Modern Mathematics by Ian Stewart
- Calculus by Spivak
- Essence of Linear Algebra by 3B1B
- Introduction to Statistical Inference by Jack C. Kiefer
- Probability and Random Processes by Geoffrey R. Grimmett, David R. Stirzaker
- Data Science from Scratch: First Principles with Python by Joel Grus
- An Introduction to Statistical Learning by Gareth James, Trevor Hastie, Robert Tibshirani, Daniela Witten
- Data Analysis: A Bayesian Tutorial by Devinderjit Sivia, John Skilling
- Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Shai Ben-David
- A Course in Machine Learning by Hal Daumé
- Do We Need Hundreds of Classifiers to Solve Real World Classification Problems?
- What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum, Review
- Kaggle courses
- Google: Machine Learning Crash Course
- fast.ai: Introduction to Machine Learning for Coders
- Coursera: Andrew Ng's Machine Learning
- Coursera: Applied Data Science with Python Specialization
- Coursera: Machine Learning Specialization
Could use more help with this section
- Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil
- How to Study Math
- Ideas in Mathematics
- Mathematics: Its Content, Methods and Meaning
- Princeton Companion to Mathematics
- Linear Algebra Explained in Four Pages
- Math for Machine Learning
- Mathematics for Machine Learning
- Explained Visually
- Statistical Methods
- Multiple Hypothesis Testing
- The 10 Statistical Techniques Data Scientists Need to Master
- Bayesian Decision Theory Made Ridiculously Simple
- Machine Learning Mastery
- Machine Learning Yearning
- Machine Learning Algorithms: Which One to Choose for Your Problem
- Google: Learn with Google AI
- The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data
- Does water kill? A call for less casual causal inferences
- ML beyond Curve Fitting: An Intro to Causal Inference and do-Calculus
- Statistical Inference for Data Science
- fast.ai: Practical Deep Learning For Coders
- Open Source Data Science Masters
- Python Data Science Handbook
- scikit-learn
- TensorFlow-Tutorials
- 39 Machine Learning Resources That Will Help You in Every Essential Step
- Machine Learning Cheatsheet
- Facets: Visualizations for machine learning datasets
- Google Dataset Search
- The 50 Best Free Datasets for Machine Learning
- Hacker's Guide to Heathcare Data
- A guide to deep learning in healthcare
- Guide to saving & hosting your first machine learning model
- AWS explained
- Why you need to improve your training data, and how to do it
- What Charts Do
- What Charts Mean
- Data cleaning challenge
- Building a simple Keras + deep learning REST API
- cheatsheets-ai