In this course we implement the most popular Machine Learning algorithms from scratch using pure Python and NumPy.

By the end of this course, you will have a deep understanding of the concepts behind those algorithms.

Each part starts with a short theory section that explains the math and concepts behind the algorithm. Then we jump to the code and implement it in a clean, object oriented style. You will get a hands-on experience with Machine Learning algorithms and feel more confident using them in your own projects.

Prerequisites

Beginner Python skills and a little bit of math knowledge (Linear Algebra, Differential Equations) is required to follow the course. NumPy knowledge can be benefitial but is not a must. If you want to have a quick refresher, you can checkout my free NumPy handbook that covers all essential functions. You can get it here.

Note

This is a collection of my ML From Scratch playlist compiled into one single video. The code for all algorithms is available on GitHub. The jupyter notebooks are available on Patreon.

Course Overview

1. KNN
2. Linear Regression
3. Logistic Regression
4. Regression Refactoring
5. Naive Bayes
6. Perceptron
7. SVM
8. Decision Tree Part 1
9. Decision Tree Part 2
10. Random Forest
11. PCA
12. K-Means
13. AdaBoost
14. LDA
15. Load Data From CSV