Mathematics for Machine Learning
In this post I have compiled great e-resources for learning Mathematics for Machine Learning.
It doesn’t matter what catches your fancy, machine learning, artificial intelligence, or deep learning; you need to know the basics of math and stats—linear algebra, calculus, optimization, probability—to get ahead.
Math is a specific, powerful vocabulary for ideas and giving a structure to the way you learn it will empower you to absorb much more of it much faster.
I have added links to MOOCs🎓, YouTube▶️ Playlists and Free e-books📚.
Understanding Mathematics
The Map of Mathematics by MajorPrep The entire field of mathematics summarised in a single map! This shows how pure mathematics and applied mathematics relate to each other and all of the sub-topics they are made from.
The Mathematics of Machine Learning by MajorPrep
How to Learn Mathematics Fast by Siraj Raval Whether you're interested in AI or you just want to do some real engineering work, you’re going to need to brush up on your math skills. In this video, I’ll describe my strategy to learn mathematics as fast as possible.
MOOC 🎓
Mathematics for Machine Learning Specialization by Imperial College London on Coursera
This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.
In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.
The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.
The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require basic Python and numpy knowledge.
At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.
Linear Algebra 🧮
Essence of linear algebra by 3Blue1Brown A geometric understanding of matrices, determinants, eigen-stuffs and more.
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 by Gilbert Strang
MIT 18.06SC Linear Algebra, Fall 2011 by Gilbert Strang
Coding The Matrix: Linear Algebra Through Computer Science Applications The course is driven by applications from areas chosen from among: computer vision, cryptography, game theory, graphics, information retrieval and web search, and machine learning.
Linear algebra by Khan Academy
Calculus 🌊
Essence of calculus by 3Blue1Brown The goal here is to make calculus feel like something that you yourself could have discovered.
Multivariable calculus by Khan Academy
Differential equations ✖️
Differential equations by 3Blue1Brown An overview of differential equations.
Differential equations by Khan Academy
Complex Numbers 📐
Imaginary Numbers are Real by Welch Labs
Statistics & Probability 🎲
Intro to Statistics by Udacity Statistics is about extracting meaning from data. In this class, we will introduce techniques for visualizing relationships in data and systematic techniques for understanding the relationships using mathematics.
Statistics 110: Probability by Joe Blitzstein, Professor of the Practice in Statistics. Harvard University, Department of Statistics
High school statistics by Khan Academy
Neural networks 🧠
Neural networks by 3Blue1Brown Visual Introduction to Neural Netorks.
Neural Networks Demystified by Welch Labs Neural Networks Demystified, Implement Neural Network from scratch using only Python.
The Math of Intelligence by Siraj Raval
Books 📚
An Introduction to Statistical Learning (with applications in R)
This book written by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani is meant for non-math students.
The TOC includes linear regression, classification, resampling methods, linear model and regularization, tree-based methods, shrinkage approaches, clustering, support vector machines, and unsupervised learning.
With interesting real-world examples and attractive graphics, this is a great text for statistical tools and techniques.
Free Book: Download here
The Elements of Statistical Learning
Authors Trevor Hastie, Robert Tibshirani, and Jerome Friedman (all three are Stanford professors) discuss supervised learning, linear methods of regression and classification, kernel smoothing methods, regularization, model selection and assessment, additive trees, SVM, neural networks, random forests, nearest neighbors, unsupervised learning, ensemble methods, and more.
Free Book: Download here
Pattern Recognition and Machine Learning
Christopher M. Bishop.
Free Book: Download here
Deep Learning
This what Elon Musk, co-founder of Tesla Motors, has to say about this definitive text written by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject”.
The authors talk about applied math and machine learning basics, deep networks and modern practices, and deep learning research.
For engineers interested in neural networks, this could well be their bible.
Free Book: Download here
Neural Networks and Deep Learning
Michael Nielsen’s free online book is a comprehensive text on the core concepts of deep learning and artificial neural networks.
The book has great interactive elements, but it does not provide solutions for the exercises. Laid out like a narrative, Nielsen holds onto core math and code to explain the key ideas.
He talks about back propagation, hyper parameter optimization, activation functions, neural networks as functional approximates, regularization, a little about convolution neural networks, etc.
Website: Link
Math Solving Platforms 🎮
Brilliant Org Brilliant creates a culture of learning around inquiry, curiosity, and openness to failure. All of our courses are written with these principles of learning in mind.
WolframAlpha The introduction of Wolfram|Alpha defined a fundamentally new paradigm for getting knowledge and answers—not by searching the web, but by doing dynamic computations based on a vast collection of built-in data, algorithms and methods. Bringing broad, deep, expert-level knowledge to everyone… anytime, anywhere.
YouTube Channels to Follow ▶️
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