Machine Learning Roadmap

Phase 1 - Learning the Python programming language

Books:

• Beginning Python From Novice To Professional - Magnus Lie Hetland

• Think Python - Allen B. Downey

• Learning Python - Mark Lutz

• Learn Python The Hard Way - Zed A. Shaw

• Programming Python - Mark Lutz

• Python 3 Object Oriented Programming - Dusty Phillips

• Functional Python Programming - Steven Lott

• Functional Progamming in Python - David Mertz

• Automate the Boring Stuff with Python - Al Sweigart

• Effective Python - Brett Slatkin

• Python Parallel Programming Cookbook - Giancarlo Zaccone

• Python Cookbook - David Beazley and Brian K. Jones

• Python Tricks: The Book - Dan Bader

• Dive Into Python 3 - Mark Pilgrim

Tutorials:

• The Python Tutorial

• Learn Python - Free Interactive Python Tutorial

• Python Tutorial - w3schools

• Python Tutorial - tutorialspoint

Courses:

• Learn Python - coursera

• Python Basics for Data Science - IBM

• Introduction to Python for Data Science - Microsoft

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Phase 2 - Calculus

Books:

• Calculus Early Transcendentals - James Stewart

• Higher Engineering Mathematics - John Bird

• Understanding Engineering Mathematics - Bill Cox

• Calculus - George B. Thomas

• Calculus - Howard Anton, Irl Bivens, and Stephen Davis

Courses:

• Calculus 1 - Khan Academy

• Calculus 2 - Khan Academy

• Multivariable Calculus - Khan Academy

• Single Variable Calculus - MIT OpenCourseWare

• Multivariable Calculus - MIT OpenCourseWare

Videos:

• Essence of Calculus - 3Blue1Brown

• Calculus - Professor Dave Explains

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Phase 3 - Discrete Mathematics

Books:

• Discrete Mathematics with Applications - Susanna S. Epp

• Discrete Mathematics and Its Applications - Kenneth H. Rosen

• Mathematics - A Discrete Introduction - Edward R. Scheinerman

Tutorials:

• Discrete Mathematics Tutorial - tutorialspoint

• Discrete Mathematics Tutorial - wisdomjobs

Courses:

• Mathematics for Computer Science - MIT OpenCourseWare

• Introduction to Discrete Mathematics for Computer Science Specialization - UC San Diego

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Phase 4 - Algorithms and Data Structures + Problem Solving

Books:

• Data Structures and Algorithms in Python - Michael T. Goodrich, Roberto Tamassia, and Michael H. Goldwasser

• Python Algorithms - Magnus Lie Hetland

• Grokking Algorithms - Aditya Y. Bhargava

• Introduction to Algorithms - Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein

• The Algorithm Design Manual - Steven S. Skiena

Tutorials:

• Data Structures and Algorithms Tutorial - tutorialspoint

• GeeksforGeeks

• HackerRank

• topcoder

• LeetCode

• coderbyte

Courses:

• Algorithms - Khan Academy

• Introduction to Algorithms - MIT OpenCourseWare

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Phase 5 - Introduction to Linear Algebra

Books:

• Elementary Linear Algebra - Howard Anton and Chris Rorres

• Linear Algebra and Its Applications - David C Lay

• Introduction to Linear Algebra - Gilbert Strang

Courses:

• Linear Algebra - Khan Academy

• Linear Algebra - MIT OpenCourseWare

Videos:

• Essence of Linear Algebra - 3Blue1Brown

• Linear Algebra - Herbert Gross

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Phase 6 - Introduction to Probability and Statistics

Books:

• Introduction to Probability and Statistics for Engineers and Scientists - Sheldon Ross

• Introduction to Statistics and Data Analysis - Roxy Peck, Chris Olsen, and Jay L. Devore

• Introductory Statistics - Neil A. Weiss

• Elementary Statistics: A Step By Step Approach - Allan G. Bluman

• Introductory Statistics - Sheldon M. Ross

• Statistics in a Nutshell - Sarah Boslaugh

• Introduction to Probability - Joseph K. Blitzstein and Jessica Hwang

• Probability and Statistics for Computer Science - Michael Baron

Courses:

• Statistics and Probability - Khan Academy

• Introduction to Probability and Statistics - MIT OpenCourseWare

• Probability and Random Variables - MIT OpenCourseWare

• Statistics for Applications - MIT OpenCourseWare

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Phase 7 - Introduction to the theory of Artificial Intelligence and Machine Learning

Books:

• Artificial Intelligence A Modern Approach - Stuart J. Russell and Peter Norvig

• An Introduction to Machine Learning - Miroslav Kubat

• An Introduction to Statistical Learning with Applications in R - Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

• Learning From Data: A Short Course - Y.S . Abu-Mostafa, M. Magdon-Ismail, and Hsuan-Tien Lin

• Introduction to Machine Learning - Ethem Alpaydin

• Machine Learning - Tom M. Mitchell

• A First Course in Machine Learning - Simon Rogers and Mark G.

Courses:

• Machine Learning - Stanford

• Artificial Intelligence - MIT OpenCourseWare

• Intro to Machine Learning - Udacity

Videos:

• Machine Learning Course - Caltech

• Introduction to Machine Learning - NPTEL

• Neural Networks - 3Blue1Brown

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Phase 8 (Final) - Applications of Machine Learning using python

Books:

• Data Science from Scratch - Joel Grus

• Deep Learning with Python - Francios Chollet

• Fundamentals of Deep Learning - Nikhil Buduma

• Hands-On Machine Learning with Scikit-Learn & TensorFlow - Aurelien Geron

• Introduction to Machine Learning with Python - Andreas C. Muller and Sarah Guido

• Machine Learning In Action - Peter Harrington

• Machine Learning in Python - Michael Bowles

• Real-World Machine Learning - H. Brink, J.W. Richards, and M. Fetherolf

• Python Machine Learning - Sebastian Raschka

Tutorials:

• Machine Learning Crash Course with TensorFlow APIs - Google

• Neural Networks and Deep Learning - Michael Nielsen

• Python Machine Learning Tutorial - python-course.eu

• scikit-learn Tutorials - scikit-learn.org

• Get Started with TensorFlow - tensorflow.org

• TensorFlow Tutorial: Deep Learning for Beginner’s - guru99

Courses:

• Machine Learning with Python: A Practical Introduction - IBM

• Applied Machine Learning in Python - University of Michigan

• Machine Learning with Python: from Linear Models to Deep Learning - MITx

Videos:

• Machine Learning with Python - sentdex

• Machine Learning - Google Developers

• TensorFlow and Deep Learning without a PhD, Part 1 (Google Cloud Next '17)

• TensorFlow and Deep Learning without a PhD, Part 2 (Google Cloud Next '17)

• Tensorflow and deep learning - without a PhD by Martin Görner