Essential Math for Machine Learning: R Edition

Essential Math for Machine Learning: R Edition
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Overview

Learn the essential mathematical foundations for machine learning and artificial intelligence.

Skills you will learn

What are the course deliverables?

More about this course

Want to study machine learning or artificial intelligence, but worried that your math skills may not be up to it? Do words like "algebra' and "calculus" fill you with dread? Has it been so long since you studied math at school that you've forgotten much of what you learned in the first place?
You're not alone. Machine learning and AI are built on mathematical principles like Calculus, Linear Algebra, Probability, Statistics, and Optimization; and many would-be AI practitioners find this daunting. This course is not designed to make you a mathematician. Rather, it aims to help you learn some essential foundational concepts and the notation used to express them. The course provides a hands-on approach to working with data and applying the techniques you've learned.
This course is not a full math curriculum. It's not designed to replace school or college math education. Instead, it focuses on the key mathematical concepts that you'll encounter in studies of machine learning. It is designed to fill the gaps for students who missed these key concepts as part of their formal education, or who need to refresh their memories after a long break from studying math.

Introduction

Equations, Functions, and Graphs

Differentiation and Optimization

Vectors and Matrices

Statistics and Probability

Eligibility criteria Up Arrow Icon

To complete this course successfully, you should have: A basic knowledge of math, some programming experience - R is preferred and a willingness to learn through self-paced study.

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