Welcome to my repository of data science, machine learning, and statistics resources. Software-specific material has to a large extent been listed under their respective overviews: R Resources & Python Resources. I also host a list of SQL Resources and datasets to practice programming. If you have any additions, please comment or contact me!

LAST UPDATED: 21-05-2018

Courses:

• Udacity: Introduction to Descriptive Statistics
• Khan Academy: Linear Algebra
• edX: Introduction to Probability: Part 1 – the Fundamentals (MIT)
• edX: Introduction to Computer Science with Python (MIT)
• Youtube: Artificial Intelligence 2010/2015 (MIT)
• Coursera: Data Science Specialization (John Hopkins)
• Coursera: Machine Learning (Stanford)
• Coursera: Applied Data Science with Python (Michigan)
• Coursera: Applied Machine Learning in Python (Michigan)
• Introduction to Statistical Learning (Hastie & Tibshirani, 2014)
• Statistical Computing for Scientists and Engineers (Notre Dame, 2017)
• Survey Data Collection and Analytics Specialization @Coursera
• UC Business Analytics R Programming Guide (Cincinatti)

Video:

• Deep Learning Book – Accompanying YouTube Video’s
• 3Blue1Brown YouTube Channel
• Deep Learning Demystified – Brandon Rohrer YouTube Channel

Books:

• Machine Learning, Neural and Statistical Classification (Michie, Spiegelhalter, & Taylor, 1994)
• Introduction to Machine Learning (Nilsson, 1998)
• Elements of Statistical Learning (Hastie, Tibshirani, & Friedman, 2001)
• Information Theory, Inference, and Learning Algorithms (MacKay, 2003)
• Data Mining Techniques for Marketing, Sales and CRM (Berry & Linoff, 2004)
• Data Mining: Practical Machine Learning Tools and Techniques (Witten & Frank, 2005)
• Gaussian Processes for Machine Learning (Rasmussen & Williams, 2006)
• Inductive Logic Programming and Its Application to the Temporal Expression Chunking Problem (Poveda & Borràs, 2007)
• Introduction to Machine Learning (Shashua, 2008)
• Modeling with Data (Klemens, 2009)
• Mining of Massive Datasets (Leskovec, Rajamaran, & Ullman, 2010)
• An Introduction to Data Science (Stanton, 2012)
• Think Bayes: Bayesian Statistics Made Simple (Downey, 2012)
• Machine Learning with R (Lantz, 2013)
• Introduction to Statistical Thought (Lavine, 2013
• Introduction to Data Technologies (Murrel, 2013)
• Introduction to Statistical Learning (James, Witten, Hastie, & Tibshirani, 2013)
• Think Stats: Exploratory Data Analysis in Python (Downey, 2014)
• Understanding Machine Learning: From Theory to Algorithms (Shalev-Schwartz & Ben-David, 2014)
• Big Data, Data Mining, and Machine Learning (Dean, 2014)
• Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, Github and More (Russel, 2014)
• Data Mining and Analysis: Fundamental Concepts and Algorithms (Zaki & Meira Jr., 2014)
• Regression Models for Data Science in R (Caffo, 2015)
• OpenIntro Statistics (Diez, Barr, & Cetinkaya-Rundel, 2015)
• Bayesian Reasoning and Machine Learning (Barber, 2016)
• Deep Learning (Goodfellow, Bengio, & Courville, 2016)
• R Programming for Data Science (Peng, 2016)
• Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Davidson-Pilon, 2016)
• Introduction to Empirical Bayes (Robinson, ???)
• Mining Massive Datasets (Leskovec, Rajaraman, & Ullman, ???) @Stanford
• A Programmer’s Guide to Data Mining: The Ancient Art of the Numerati (Zacharski, ???)
• Data Science Live Book (Casas, 2017)
• Statistical Foundations of Machine Learning (Bontempi & Taieb, 2017)
• Machine Learning – Complete Guide (Wikipedia, ???)
• Foundations of Data Science (Adhikari & DeNero, ????) @Berkeley
• Foundations of Data Science* (Blum, Hopcroft, & Kannan, 2017) @Cornell
• Computer Age Statistical Inference (Efron & Hastie, 2017)
• A Course in Machine Learning (Daumé III, 2017)
• R for Data Science (Grolemund & Wickham, 2017)
• Machine Learning God (Stavrev, 2017)
• Overview of survey books and courses via freerangestatistics.info
• Feature Engineering and Selection (Kuhn & Johnson, 2018)

Sentiment Lexicons:

• Sentiment Lexicons for 81 Languages: Sentiment Polarity Lexicons (Positive vs. Negative)
• SentiWordNet: Sentiment WordNet Project
• Thai Sentiment Analysis Toolkit: Positive, negative and swear words in Thai
• German Sentiment Analysis Toolkit: 3468 German words sorted by sentiment
• Vader Lexicon: Lexicon use for the Vader Sentiment Algorithm
• Opinion Lexicon: For Sentiment Analysis
• VerbNet: VerbNet Lexicon, Version 2.1

Cheatsheets:

• Google Developers Machine Learning Glossary
• Neural Network Architectures Cheatsheet by Asimovinstitute.org
• Neural Network Cell Inner Processes Cheatsheet by Asimovinstitute.org
• Neural Network Full Process Cheatsheet by Asimovinstitute.org
• Tensorflow Cheatsheet by Altoros.com
• Statistics & Probability Cheatsheet
• Statistics Cheatsheet by MIT
• Linear Algebra Cheatsheet by minireference.com
• Machine Learning Algorithms Cheatsheet by Scikit-Learn.org
• Machine Learning Algorithms Cheatsheet by Microsoft Azure
• Machine Learning Equations & Tricks Cheatsheet by github.com/soulmachine
• Supervised Learning Python Implementations by github.com/rcompton
• Machine Learning Algorithms R Implementation by Ajitesh Kumar

Other:

• Google Fonts – huge collection of text fonts
• Checkmycolours.com – check whether your colours have enough contrast
• Vischeck.com – check whether your images are colorblind-friendly
• Coblis – Color Blind Simulation
• Color Oracle – color blind simulation
• Chrome color enhancer – customizable color filter for website browsing