50+ Best Free Resources to Learn Mathematics for Machine Learning

The Mathematics of Machine Learning - Best books, pdf, cheats, courses, youtube playlist to learn essential mathematics for machine learning

Why people hate Maths? If anyone wants to share their views on this question, please share it wherever you find this post. If you hate maths or not, this reasonable and resourceful guide is for you. In this post, We've curated the brain-friendly and best free resources to learn essential mathematics for machine learning. Resources covered in this post includes websites, books, free courses, cheat sheets, pdf, github repos and youtube playlist for linear algebra, calculus, probability, statistics, optimization methods and a lot more.

Let's take a deep dive into each topic...

1. Linear Algebra & Trigonometry for Machine Learning

👉 Why you should learn linear algebra for machine learning?

In machine learning, most of the time we deal with scalars and vectors, and matrices. For example in logistic regression, we do vector-matrix multiplication. Sometimes we do clustering of input by using spectral clustering techniques, and for this, we need to know eigenvalues and eigenvectors. Linear algebra is also used in data preprocessing, data transformation, dimensionality reduction, and model evaluation.

👉 What are some of the core topics you should learn in linear algebra?

Topics such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Eigen decomposition of a matrix, LU Decomposition, QR Decomposition/Factorization, Symmetric Matrices, Orthogonalization & Orthonormalization, Matrix Operations, Projections, Eigenvalues & Eigenvectors, Vector Spaces and Norms are needed for understanding the optimization methods used for machine learning

👉 What are some of best free resources to learn Linear Algebra?


YouTube Playlist and Lectures:

💨 Youtube Playlist: Trigonometry by Khan Academy

💨 Youtube Playlist: Linear Algebra by Dr Trefor Bazett



Github & Cheats:

💨 Cheat Sheets: Algebra Cheats by Paul Dawkins

2. Probability and Statistics for Machine Learning

👉 What's the use of probability and statistics in machine learning?

Probability helps you to manage the uncertainty. Uncertainty means working with imperfect or incomplete information. And in Machine Learning, we build predictive models from uncertain data. But we can manage uncertainty using the tools of probability. Whereas Statistics help you to count well, normalize well, obtain distributions, find out the mean of your input feature, and its standard deviation. That’s why knowledge of Probability and Statistics is important for machine learning.

👉 What are some of the core topics you should learn in stats and probability?

Some of the fundamental Statistical and Probability Theory needed for ML are Combinatorics, Probability Rules & Axioms, Bayes’ Theorem, Random Variables, Variance and Expectation, Conditional and Joint Distributions, Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian), Moment Generating Functions and more.

👉 What are some of the best free resources to learn Probability and Statistics?


YouTube Playlist and Lectures:

💨 Youtube Playlist: Introduction to Probability by MIT



Github & Cheats:

💨 Cheat Sheets: Statistics Cheats by MIT

3. Calculus for Machine Learning

👉 What's the use of calculus in machine learning?

Calculus helps us to explain the relationships between input and output variables. And Multivariate Calculus comes into the picture when you deal with a lot of features and huge data. That’s why familiarity with multivariate calculus is essential for building a machine learning model.

👉 What are some of the core topics you should learn in calculus?

Some of the necessary topics include Differential and Integral Calculus, Partial Derivatives, Vector-Values Functions, Directional Gradient, Hessian, Jacobian, Laplacian and Lagragian Distribution.

👉 What are some of the best free resources to learn Calculus?


YouTube Playlist and Lectures:

💨 Youtube Playlist: Essence of Calculus by 3Blue1Brown

💨 Youtube Playlist: Calculus 1, Calculus 2, Calculus 3 and Calculus 4 by Dr Trefor Bazett


eBooks & Cheats:

4. Optimizations Methods & Other Topics for Machine Learning

👉 What's the use of optimization in machine learning?

Optimization methods are important to understand the computational efficiency and scalability of our Machine Learning Algorithm. In the end, mostly all Machine learning algorithms come down to some optimization tasks.

👉 What are some core topics you should learn in optimization methods? 

Knowledge of data structures (Binary Trees, Hashing, Heap, Stack etc), Dynamic Programming, Randomized & Sublinear Algorithm, Graphs, Gradient/Stochastic Descents and Primal-Dual methods are needed.

Other important disciplines can include continuous functions limits, information theory, real and complex analysis (Sets and Sequences, Topology, Metric Spaces, Single-Valued and Continuous Functions, Limits, Cauchy Kernel, Fourier Transforms) and Manifolds. 

👉 What are some of the best free resources to learn Optimization and other remaining topics?


YouTube Playlist and Lectures:

Course, eBook & Research Paper:

👉 All in One Maths Resources (Algebra, Trigonometry, Calculus, Probability, Statistics, Optimization and more, everything at one place)

Despite the immense possibilities of Machine Learning and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. For that reason, we have curated and shared some of the best resources to learn mathematics for machine learning . We hope these resources will be helpful to you in learning and implementing maths behind machine learning. So, that's it for now. If you have any doubt or questions or suggestion, feel free to share your it with us wherever you're following us.
May 16, 2021


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