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
- KNN
- Linear Regression
- Logistic Regression
- Regression Refactoring
- Naive Bayes
- Perceptron
- SVM
- Decision Tree Part 1
- Decision Tree Part 2
- Random Forest
- PCA
- K-Means
- AdaBoost
- LDA
- Load Data From CSV
