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 include websites, books, free courses, cheat sheets, GitHub repos, and a YouTube playlist for linear algebra, calculus, probability, statistics, optimization methods, and a lot more.

Let’s take a deep dive into each topicâ€¦

If you want to learn all the topics from a single book or a single course then the below given resources are the best for you.

Table Of Contents đź‘‰

## Resources To Learn Maths For Machine Learning

**Mathematics for Machine Learning (Free eBook)****Maths of Machine Learning by MIT (Course)****Linear Algebra, Calculus, and Probability of Machine Learning (YouTube Playlist)****Maths for Machine Learning (YouTube Playlist)****Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Machine Learning (eBook)**

**And If you want to learn a specific topic in Maths for Machine Learning then go through the list given below**

**Recommended Stories:**

- Want To Learn About Machine Learning Algorithms With Python? If Yes, Then You Must Check Out This Post: Machine Learning Algorithms In Python

- Want To Learn Machine Learning In An Interesting Way? If Yes, Then Check Out This Rarely Seen Free Resources: Best Resources To Learn Machine Learning

**Linear Algebra 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, Symmetric Matrices, Matrix Operations, Projections, Eigenvalues & Eigenvectors, Vector Spaces and Norms are needed for understanding the optimization methods used for machine learning

**Best Free Resources To Learn Linear Algebra For Machine Learning**

**Best Websites To Learn Linear Algebra For Machine Learning**

- Learn Algebra for Machine Learning with Math is Fun
- Linear Algebra with the Learning Machine
- Introduction to Linear Algebra for Applied Machine Learning with Python

**Best YouTube Videos To Learn Linear Algebra For Machine Learning**

- Essence of Linear Algebra by 3Blue1Brown
- Trigonometry by Khan Academy
- Linear Algebra by Dr Trefor Bazett
- Trigonometry Fundamentals by 3Blue1Brown
- Linear Algebra for Machine Learning By Applied AI Course

**Best Courses To Learn Linear Algebra For Machine Learning**

- Linear Algebra for Machine Learning by Khan Academy
- Gilbert Strang lectures on Linear Algebra (MIT)
- Coding The Matrix: Linear Algebra Through Computer Science Applications

**Best Books To Learn Linear Algebra For Machine Learning**

- Linear Algebra Abridged by Sheldon Axler
- Introduction to Applied Linear Algebra â€“ Vectors, Matrices, and Least Squares
- Linear Algebra for Machine Learning PDF
- Deep Learning Book By Ian Goodfellow and Yoshua Bengio and Aaron Courville

**Best Linear Algebra Cheat Sheet For Machine Learning**

- Matrix Calculus Cheat Sheet by Stanford University
- Trigonometry Cheats by Paul Dawkins
- Linear Algebra Cheat Sheet by Paul Dawkins

**Probability And Statistics For Machine Learning**

**What’s the use of probability and statistics in machine learning?**

Probability helps you to manage uncertainty. Uncertainty means working with imperfect or incomplete information. In Machine Learning, we build predictive models from uncertain data.

However, we can manage uncertainty using the tools of probability. Whereas Statistics help you to count well, normalize well, obtain distributions, and find out the mean of your input feature, and its standard deviation.

Thatâ€™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 Theories needed for ML are Combinatorics, Probability Rules & Axioms, Bayesâ€™ Theorem, Random Variables, 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?**

**Best Websites, YouTube Videos, And Courses To Learn Probability And Statistics For Machine Learning**

- Seeing Theory – A Visual Introduction to Probability and Statistics
- Learn Probability and Statistics of Machine Learning with Math is Fun
- Statistics 110 – Probability by Harvard University
- Introduction to Probability by MIT
- Introduction to Statistics by Udacity
- Probabilistic Systems Analysis and Applied Probability by MIT
- Statistics and Probability for Machine Learning by Khan Academy

**Best Books To Learn Probability And Statistics For Machine Learning**

- Introduction to Probability
- An Introduction to Statistical Learning for Machine Learning
- Probability Theory: The Logic of Science by E. T. Jayne
- The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman

**Best Probability And Statistics Cheat Sheets For Machine Learning**

- A Concrete Introduction to Probability Using Python By Peter Norvig
- Probability and Statistics Cheat Sheets for Machine learning by Stanford university
- Statistics Cheats by MIT

**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.

Thatâ€™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 Lagrangian Distribution.

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

**Best Websites And YouTube Playlists To Learn Calculus For Machine Learning**

- Learn Calculus for machine Learning with The Learning Machine
- Learn Calculus with Math is Fun
- Essence of Calculus by 3Blue1Brown
- Calculus 1, Calculus 2, Calculus 3 and Calculus 4
- Single Variable Calculus by Penn Professor Robert Ghrist
- Mathematics of Machine Learning – Multivariate Calculus by Imperial College London

**Best Courses To Learn Calculus For Machine Learning**

- Single Variable Calculus by MIT
- Multivariable Calculus by MIT
- Calculus by Gilbert Strang
- Introduction to Calculus Volume I and Volume II by J.H. Heinbockel
- Calculus Cheat Sheet by Paul Dawkins

**Recommended Stories:**

- Are You Looking For Open Source Machine Learning GitHub Projects And Repos? If Yes, Then You Must Check Out This Updated List: Best GitHub Repositories For Machine Learning

- Are You Looking For the Best Machine Learning Blogs To Follow? If Yes, Then You Must Check Out This Post: Expert’s Recommended Machine Learning And Deep Learning Blogs

**Optimization Methods & Other Topics For M**L

**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 is needed.

**What are some of the best free resources to learn Optimization?**

**Best Website, YouTube Playlist, Books And Courses To Learn Optimization Methods And Other Machine Learning Topics**

- Learn Optimization Methods of Machine Learning with the Learning Machine
- How Optimization for Machine Learning Works
- Optimization for Machine learning by DeepMind
- Optimization Methods for Machine Learning
- Convex Optimization for Machine learning
- A Survey of Optimization Methods from a Machine Learning Perspective

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 essential mathematics for machine learning. We hope this curated list of resources for learning machine learning math will be helpful to you.

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.

## Frequently Asked Questions:

### What Kind Of Math Is Needed For Machine Learning?

Four key mathematical concepts are essential to machine learning. They are Statistics, Linear Algebra, Calculus, and Probability.

### Do You Need To Be Good At Math For Machine Learning?

For beginners, you don’t need a lot of Mathematics to start doing Machine Learning. The fundamentals should be an additional benefit and helpful in the long run. You can learn the basics of maths behind machine learning from the above given brain friendly free resources.

### Is Mathematics For Machine Learning Hard?

No, It’s not hard. If you think it’s hard, Just go through the above given brain friendly maths for machine learning resources.

### What Are The Best Resources To Learn Essential Mathematics For Machine Learning?

Mathematics for Machine Learning PDF by Marc Peter Deisenroth, Maths of Machine Learning Course by MIT, Linear Algebra, Calculus and Probability of Machine Learning YouTube Playlist by Weights And Biases, etc, Are The Best Resources To Learn Essential Mathematics For Machine Learning.