So you want to learn the Mathematics for Machine Learning?

Well, for Machine Learning or Deep Learning and AI, a thorough mathematical understanding is not an option.

**I know the options out there**; prerequisites and the skills you need to become successful in Machine Learning and AI.

If you want to learn Machine Learning, these classes will help you to master the mathematical foundation required for writing programs and algorithms for Machine Learning, Deep Learning and AI.

My goal in this piece is to help you find the resources to gain good intuition and get you the hands-on experience you need with coding neural nets, stochastic gradient descent, and principal component analysis.

## 5 Best Courses to Learn Mathematics for Machine Learning, Deep Learning and AI.

Content Insight

I’ve compiled these classes to give a well-intended advice about learning Mathematics for Machine Learning, Deep Learning and AI.

**So**, without further ado, let’s get started.

### — Mathematics for Machine Learning: Linear Algebra

This course is part of a machine learning specialization ( sectioned below ) designed by Imperial College London and delivered via Coursera.

This course equips learners with the functional knowledge of linear algebra required for machine learning.

You will learn to work with vectors and matrices and also understand the knotty problem of eigenvalues and eigenvectors.

#### Is it right for you?

This beginner level course is suitable for learner with high-school level knowledge in mathematics.

By the end, you will be equipped with skills to work with Eigenvalues And Eigenvectors, Basis (Linear Algebra), Transformation and Matrix Linear Algebra.

### — Mathematical Foundation For Machine Learning and AI

This course is designed by Edunoix and delivered via Udemy to equip learners with the core mathematical concepts for machine learning and implement them using both R and Python.

Through the guided series of lectures, you will learn the mathematical concepts to implement algorithms in Python.

You will also understand the key concepts for solving real world problems with machine learning.

#### Is it right for you?

This course is suitable for learners with the basic knowledge of Python or R as the concepts are coded in both Python and R.

Upon the completion, you will have become highly prepared to build your own algorithms with confidence required for writing programs for AI and ML.

### — Vector Calculus for Engineers

This course is created by The Hong Kong university of Science and Technology, taught by Jeffrey R. Chasnov and delivered via Coursera.

Through the series of guided lectures, you will dive deeper in to learn scalar, vector fields, differentiating fields and integrating fields, including but not limited to theorems.

#### Is it right for you?

This course is suitable for intermediate-level learners aiming to gain higher understanding of vector calculus.

By the end, you will be highly skilled to work with Multivariable Calculus., Engineering Mathematics and Calculus Three.

### — Mathematics for Machine Learning: Multivariate Calculus

This course is part of a machine learning specialization ( sectioned above ) designed by Imperial College London and delivered via Coursera.

This interactive course is designed to help you gain an intuitive multivariate calculus and help you understand the process of building the common machine learning techniques.

#### Is it right for you?

This beginner level course is suitable for learners who are looking for a refresher to become prepared for advanced machine learning courses.

By the end, you will have become familiar to work with Linear Regression, Vector Calculus, Multivariable Calculus and Gradient Descent.

### — Mathematics for Machine Learning Specialization

This three-course specialization by Imperial College London aims to solidify your math skills to prepare you for learning advanced concepts in Machine Learning and Data Science.

You will not only be able to overcome any learning blocks but also get you up to speed in the underlying mathematics to build computational understanding.

#### Is it right for you?

This specialization is suitable for **beginners** who want to gain the prerequisite mathematical knowledge, related to Data Science, Machine Learning and Deep Learning.

##### Closing Notes

Learning Mathematics for Machine Learning is exceedingly hard but not impossible.

I’ve also got a few practical reads for you. One about Free Machine Learning Courses on the Internet and one about Learning Machine Learning for Finance.

I hope your journeys will go as you hope, and that the resources listed above will equip you with the Machine Learning core skills ( Mathematical Thinking, Learning TensorFlow, Pandas, Statistics and more ) you desire to build.

###### Thanks for making it to the end 😉

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