Tutorials

How to Navigate

If you’re a complete beginner or have some fragmented knowledge and want to gain a comprehensive understanding of machine learning, I recommend starting from the top of the page and working your way down. This will help you build a smooth and cohesive understanding of the topic. However, if you’re only interested in a specific area, feel free to jump to it. Each course is designed to stand alone, with the flexibility to revisit topics as needed.

The course overview lets you see everything we’ll cover—just click on any topic to jump straight to the page that interests you.

Introduction

This course introduces the fundamental concepts of machine learning.
You’ll learn key definitions like What is Artificial Intelligence? and What does it mean for a machine to learn?
We’ll also explore the main types of machine learning, giving you a solid overview of the field.

To help you get started hands-on, the course includes a short programming tutorial along with a step-by-step guide to installing Python. No prior experience needed—this course is designed for complete beginners.

Course Overview

Linear Algebra

Course Overview

References

This course reflects my personal journey into the world of machine learning. The concepts, mathematics, and code I explain throughout are based on my own understanding, built from the references listed below. These materials have been my go-to from the beginning—they’re how I learned machine learning, and I still turn to them when I need clarity on a complex concept.

While these books and materials often explore topics in greater mathematical detail and offer various perspectives on machine learning, my goal here is to bring those insights together in a way that’s clear, approachable, and practical for learners. Most of the references I’ve used are freely available online, and I’ve marked them accordingly. If you’d like to dive deeper into any topic, I highly recommend checking them out.

KADOMIN