The beginner's guide to the math for machine learning

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The math for machine learning always scared me.

Until...

This year when I across these free resources which helped me in a massive way!

Here's everything you need to know about math for machine learning and resources that you can learn from.

Before diving into the math, I suggest first having solid programming skills.

In Python, these are the concepts which you must know:

  • Object oriented programming in Python : Classes, Objects, Methods
  • List slicing
  • String formatting
  • Dictionaries & Tuples
  • Basic terminal commands
  • Exception handling

If you want to learn these concepts for python, these courses are freecodecamp could be of help to you.

You need to have really strong fundamentals in programming, because machine learning involves a lot of it.

It is 100% compulsory.

Another question that I get asked quite often is when do should you even start learning the math for machine learning?

Math for machine learning should come after you have worked on some projects, doesn't have to a complex one at all, but one that gives you a taste of how machine learning works in the real world.

image.png Here's how I do it, I look at the math when I have a need for it.

For instance I was recently competing in a kaggle challenge.

I was brainstorming about which activation function to use in a part of my neural net, I looked up the math behind each activation function and this helped me to choose the right one.

One more thing before we look into the resources, I highly recommend that you take this course.

It goes over machine learning without any of the math, this will get you more comfortable with machine learning.

The topics of math you'll have to focus on

  • Linear Algebra
  • Calculus
  • Trigonometry
  • Algebra
  • Statistics
  • Probability

Now here are the math resources and a brief description about them.

Neural Networks

A series of videos that go over how neural networks work with approach visual, must watch.

Seeing Theory

This website gives you an interactive to learn statistics and probability

Gilbert Strang lectures on Linear Algebra (MIT)

They're 15 years old but still 100% relevant today! Despite the fact these lectures are for freshman college students ,I found it very easy to follow👌

Essence of Linear Algebra

A beautifully crafted set of videos which teach you linear algebra through visualisations in an easy to digest manner watch?

Essence of calculus

A beautiful series on calculus, makes everything seem super simple

The math for Machine learning e-book

This is a book aimed for someone who knows quite a decent amount of high school math like trignometry, calculus, I suggest reading this after having the fundamentals down on khan academy.

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Comments (1)

Bijen Patel's photo

Great post Pratham. 100% agree on the programming and math fundamentals for machine learning. This is why I wrote a guide to the popular Introduction to Statistical Learning textbook here!