# Maths for Machine Learning, Deep Learning & Ai Courses

Learning Mathematics for Machine Learning & Ai can be intimidating and this is why you must start with the basics or get a refresher to build Mathematical Intuition.

So you want to learn Mathematics for Machine Learning?

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

We know the options out there; prerequisites and the skills you need to become successful in Machine Learning and AI. Therefore, we’ve compiled these resources to provide intentional support to the learners who want to upgrade their skills.

These resources will help you gain good intuition and provide you with the hands-on experience you need with coding neural nets, stochastic gradient descent, and principal component analysis.

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## Top 5 Mathematics for Machine Learning & AI Courses

These classes are suitable for beginners to master the mathematical foundation required for writing programs and algorithms for Machine Learning, Deep Learning and AI.

### — Mathematics for Machine Learning: Linear Algebra

This course is part of a machine learning specialization 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.

#### What you will learn?

• Eigenvalues And Eigenvectors
• Basics (Linear Algebra)
• Transformation Matrix
• Linear Algebra

Is it right for you?

This beginner-level course is suitable for learners 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.

### — 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 into learning scalar, vector fields, differentiating fields and integrating fields, including but not limited to theorems.

#### What you will learn?

• Vectors
• Differentiation
• Integration and Curvilinear Coordinates
• Multivariable integration, polar, cylindrical and spherical coordinates
• Line and Surface Integrals

#### Is it right for you?

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

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

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### — 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.

#### What you will learn?

• Mathematical Concepts for AI and Machine Learning
• Implement algorithms in Python
• Solve for real-world

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

### — 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 helps you gain an intuitive multivariate calculus and helps you understand building the common machine learning techniques.

#### What you will learn?

• Linear Regression
• Vector Calculus
• Multivariable Calculus

#### 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 overcome any learning blocks, and also get you up to speed in the underlying mathematics to build computational understanding.

#### What you will thoroughly learn?

• Calculus, Multivariate Calculus
• Multivariate chain rule and its applications
• Taylor series and linearisation
• Optimisation
• Regression

#### 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.

Notable Mentions

Closing Notes

You need to have a lot of experience with linear algebra for Machine Learning, Deep Learning and Ai roles.

You must be able to use machine learning algorithms to squeeze every last bit out of vector spaces and matrix mathematics.

Learning Math can be challenging, but starting with beginner-level courses, you can become equipped with the skills to learn the advanced topics.

I hope your journeys will go as you hope, and that the resources listed above will equip you with the Machine Learning core skills, including Mathematical Thinking.

We hope your journeys will go as you hope, and that the resources listed in this article will equip you for Mathematical Thinking.