# 50+ Free Resources To Learn Mathematics For Machine Learning

Why people hate Maths? If anyone wants to share their views on this question, please share it by tagging us (@TheInsaneApp) 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…

## Best Free Books, Courses, Github Repos And Cheat Sheets Required To Learn Essential Mathematics For Machine Learning

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.

### Maths behind Machine Learning – (Resources for Algebra, Trigonometry, Calculus, Probability, Statistics, Optimization and more, everything at one place)

#### 5. 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

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### 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

### 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, Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian), Moment Generating Functions and more.

### 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?

##### Best Books To Learn Calculus For Machine Learning (PDF)
###### 49. Calculus Cheat Sheet by Paul Dawkins

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### Optimizations Methods & Other Topics For ML

#### 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.

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

##### Best Courses And Books To Learn Optimization Methods And Other Machine Learning Topics:
###### 55. 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 these 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.