Resources

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Table of Contents

1. Books
2. Courses
3. Videos
4. Web pages

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Books

• An Introduction to Statistical Learning, Second Edition, 2021, by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
• The Elements of Statistical Learning, 2009, 2nd edition, by Trevor Hastie Robert Tibshirani, Jerome Friedman.
• Probabilistic Machine Learning - An Introduction, 2021, by Kevin Patrick Murphy.
• Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow, 2022, 3rd edition, by Aurélien Géron.
• Pattern Recognition and Machine Learning, 2006, by Christopher Bishop.
• Convex Optimization, 2004, by Lieven Vandenberghe, Stephen Boyd.
• Reinforcement Learning: An Introduction, 2018, Second Edition, by Richard S. Sutton and Andrew G. Barto.
• Python Data Science Handbook, 2016, by by Jake VanderPlas.
• Mathematic for Machine Learning
• Matrix Methods in Data Mining and Pattern Recognition
• Think Stats, 2014, by Allen B. Downey
• Practical Statistics for Data Scientists, 2017, by Peter Bruce, Andrew Bruce
• The Kaggle Book, 2022, by Konrad Banachewicz, Luca Massaron, Anthony Goldbloom
• Data Visualisation: A Handbook for Data Driven Design, 2016, by Andry Krik
• Truthful Art, The: Data, Charts, and Maps for Communication, 2016, by Alberto Cario
• Understanding Machine Learning: From Theory to Algorithms
• Machine Learning For Dummies
• Master Machine Learning Algorithms
• Python for Data Analysis, 2012, by Wes McKinney

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Courses

• CS229 - Machine Learning, Introduction to machine learning and statistical pattern recognition from Stanford.
• Machine Learning Specialization This is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online which covers fundamentals of machine learning
• Machine Learning Crash Course — Google AI This course explains the fundamentals of machine learning through a package of courses that include interactive visualizations of algorithms in operation, real-world case studies, literature developed specifically for ML beginners, video lectures by Google researchers, and real-world examples.
• Data Science Foundations: Python Scientific Stack , covers the major packages used throughout the data science process: numpy, pandas, matplotlib, scikit-learn, and others.
• Applied Data Science with Python Specialization
• Linear Algebra - MIT, Gilbert Strang
• Pandas Bootcamp - Udemy

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Videos

• 3Blue1Brown - some combination of math and entertainment. Difficult problems made simple with great animations.
• StatQuest - breaks down the major methodologies into easy to understand pieces.
• Krish Naik

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Web pages

• Kaggle mini courses - Gain the skills you need to do independent data science projects.
• Data Science course SBU, Fall 2021 - This course is an introduction to data science. It is an overview of the latest primary knowledge and skills in the data science criteria, focusing on the basics of linear algebra, probability and statistics, first steps of working with data, data wrangling, exploratory data analysis, visualization techniques, and the essentials of supervised learning, time series analysis, and unsupervised methods. There are no particular prerequisites, and we tried to cover everything to our potential.
• DataToViz
• CS 229 - Machine Learning

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