What is data science and what you need to know to be a data scientist?
Joel Grus, in his book writes “There’s a joke that says a data scientist is someone who knows more statistics than a computer scientist and more computer science than a statistician. (I didn’t say it was a good joke.)”
A set of good books to get started:
Data Science from Scratch: First Principles with Python by Joel Grus
Doing Data Science: Straight Talk from the Frontline by Cathy O’Neil and Rachel Schutt
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney
Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition, by Aurélien Géron
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Trevor Hastie, Robert Tibshirani, Jerome Friedman
Deep Learning with Python 2nd edition by Francois Chollet
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition, by
The Hundred-Page Machine Learning Book by Andriy Burkov
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Valliappa Lakshmanan,Sara Robinson, Michael Munn
Books on General AI-Machine Learning
Rebooting AI: Building Artificial Intelligence We Can Trust by Gary F. Marcus,Ernest Davis
Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell
Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell
Superintelligence: Paths, Dangers, Strategies by Nick Bostrom
Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark
AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee
Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans,Avi Goldfarb
Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty H. James Wilson
The Book of Why: The New Science of Cause and Effect by Judea Pearl Dana Mackenzie
Weapons of Math Destruction by Cathy O’Neil
The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do by Erik J. Larson
The Ai-First Company: How to Compete and Win with Artificial Intelligence by Ash Fontana
Real World AI : A Practical Guide for Responsible Machine Learning by Alyssa Simpson Rochwerger, Wilson Pang
Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence by Kate Crawford
Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World by Cade Metz
Framers: Human Advantage in an Age of Technology and Turmoil by Kenneth Cukier, Viktor Mayer-Schönberger,Francis de Véricourt
Newsletters:
Exponential View by Azeem Azhar
Deep Learning Weekly
Import AI by jack@jack-clark.net @jackclarksf
Inside AI by Rob May
Machine Learning Yearning by Andrew Ng
The Sequence of AI Knowledge
Online courses
Yann LeCun’s Deep Learning Course at CDS
Full Stack Deep Learning Learn Production-Level Deep Learning from Top Practitioners
Stanford CS224N: Natural Language Processing
Stanford CS221 Artificial Intelligence
Stanford CS221 Artificial Intelligence video Lectures
Stanford CS221 Artificial Intelligence Cheat Sheets
Stanford CS229 Machine Learning
Stanford CS229 Machine Learning video Lectures
Stanford CS229 Machine Learning Cheat Sheets
Stanford CS230: Deep Learning
Stanford CS230 Deep Learning video Lectures
Stanford CS230 Deep Learning Cheat Sheets
Stanford CS234: Reinforcement Learning
Introduction to Deep Learning STAT 157, UC Berkeley, Spring, 2019, instructors Alex Smola and Mu Li
CS294-158 Deep Unsupervised Learning Spring 2019 , Berkeley, Instructors: Pieter Abbeel, Peter Chen, Jonathan Ho, Aravind Srinivas
Advanced Deep Learning and Reinforcement Learning, Deep Mind
Practical Deep Learning For Code – fast.ai Free
Spinning Up in Deep RL OpenAI
deeplearning.ai by Andrew Ng on Coursera. You have to pay for this course. But you can access the videos by selecting Enrol and then Audit. Coursera has other Machine learning courses for free.
Deep Learning Nanodegree by Sebastian Thrun and others on Udacity. You have to pay for this course. In Udacity you can find also free courses, such as the Deep Learning course by Google.
Oxford Deep NLP 2017 course
Berkeley Deep Reinforcement Learning 2017
Stanford CS231n: Convolutional Neural Networks for Visual Recognition
Foundations of Data Science: Computational Thinking with Python, Berkeley by Ani Adhikari, John DeNero, David Wagner
Google Machine Learning and AI courses
Depth First Learning DFL is a compendium of curricula to help you deeply understand Machine Learning.
Data Science and Robots, an online data science course by Brandon Rohrer (Data Scientist at Facebook)
Hardware Accelerators for Machine Learning (CS 217), Stanford University, fall 2018
A (Long) Peek into Reinforcement Learning
Spinning Up in Deep RL, from OpenAI, a lot of info on Reinforcement Learning
Amazon AWS Machine Learning courses
Lex Fridman on YouTube
MIT Deep Learning
MIT GitHub
Backpropagation explained by Lex Fridman
Neural Networks: Feedforward and Backpropagation Explained & Optimization
Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
Introduction to Machine Learning by Ethem Alpaydin
Introduction to Machine Learning by Alex Smola (free download)
Pattern Recognition and Machine Learning by Christopher Bishop
Programming Collective Intelligence Toby Segaran
Bayesian Reasoning and Machine Learning by David Barber
Bayesian Data Analysis by Andrew Gelman and John B. Carlin
Mathematics for Machine Learning, by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto, Second Edition, MIT Press, Cambridge, MA, 2018 (free PDF download)Papers with Code, selected machine learning papers with code
Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman and Jennifer Hill
The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie and Robert Tibshirani
Statistical Inference by George Casella
Advanced Data Analysis from an Elementary Point of View by Cosma Rohilla Shalizi (draft free download)
Introductory Statistics Barbara Illowsky,Susan Dean (free download)
OpenIntro Statistics by David M Diez, Christopher D Barr, Mine Cetinkaya-Rundel (free download)
Introduction to Probability by Charles M. Grinstead and J. Laurie Snell
Introduction to Probability Models by Sheldon M. Ross
A First Course in Probability by Sheldon Ross
Linear Algebra and Calculus
Active Calculus Matthew Boelkins, David Austin, Steven Schlicker (free download)
Convex Optimization by Stephen Boyd and Lieven Vandenberghe (free download)
Linear Algebra and Its Applications by Gilbert Strang
Linear Algebra by Jim Hefferon
Linear Algebra by David Cherney, Tom Denton and Andrew Waldron (free download)
Linear Algebra Done Wrong by Sergei Treil (free download)
Deep Learning Book Notes, Chapter 2: Linear Algebra for Deep Learning
Presenting the information
The Visual Display of Quantitative Information by Edward R. Tufte
Some of my posts
Machine Learning Research Groups
Machine Learning as a service
Weapons of Math Destruction
Machine Learning and Its evolution
Deep Learning