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machine-learning-roadmap's Introduction

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:

Courses:


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:

Videos:


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:

Courses:


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:

Courses:


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:

Videos:


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:


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:

Videos:


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:

Courses:

Videos:

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