Math for Machine Learning

Name: Math for Machine Learning
Rating: 4.3 (19 reviews)

4.316 hours1K learners

Explore the mathematical concepts driving basic machine learning techniques and acquire the skills needed to excel in regression tasks.

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Edvancium

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What you'll learn

With this course, you will:

learn the essentials of sets and numerical functions;
discover how probability is related to linear regression and classification tasks;
get familiar with optimization problems and figure out what derivatives have to do with them;
explore vectors and matrices and find out how they can help with multiple linear regression problems;
examine gradient descent, a focal machine learning optimization method, how it can be applied to logistic regression, and take notes of its pros and cons.

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Learn by doing

# 1

Apply knowledge into practice

You already know the theory. Now it's time to code like you do at work—in a professional IDE, with real project constraints, solving problems that actually matter. Welcome to software engineering as it should be.

# 2

Navigate complexity with surgical precision

Most developers waste months learning random concepts without seeing how they connect. Our interactive Knowledge Map fixes this. It shows exactly how every programming concept relates to others, helping you build a structured mental model of coding.

# 3

Copy the best. Then improve.

Here's what top engineers do that others don't: they study other people's code obsessively. When you get stuck on Hyperskill, you can explore solutions published by other developers. See their exact code. Understand their approach. Learn their tricks.

# 4

Code review that actually makes you better

We stripped code review down to what actually matters: does your solution work? Have you handled the edge cases? Is there a cleaner way to write this? Hyperskill acts like a competent reviewer who actually tests your code. Not genius-level analysis, not architecture debates — just solid feedback on making your code better.