The world's best Machine Learning course – Machine Learning Algorithms from Scratch

I would like to present to your attention a course that I recently finished writing on Stepik – Machine Learning Algorithms from Scratch.

In this course you will implement all the basic machine learning algorithms in pure Python (+ NumPy and Pandas). At the same time, the main emphasis in presenting the material is on algorithms with specifications. programming, and not with TZ. mathematics. Although the basic mathematical concepts are also given.

The course is free and is located on the Stepik platform: https://stepik.org/course/68260/promo

Course topics:

1. Linear models:

   1. Linear regression

   2. Logistic regression

2. Nearest neighbors method, kNN (classification and regression)

3. Decision trees (classification and regression)

4. Ensembles:

   1. Random Forest (Regression and Classification)

   2. Bagging (regression and classification)

   3. Gradient boosting (regression and classification)

5. Clustering:

   1. K-means method (K-Means)

   2. Hierarchical Agglomerative Clustering

   3. DBSCAN

6. Dimensionality reduction (Principal Component Analysis, PCA)

Whenever possible, I will add less popular topics from classic ML (recommendations, SVM, etc.)

The course is designed for those who are already familiar with the basics of machine learning and want to understand in more detail the implementation of classical machine learning algorithms. In addition to machine learning, you will also need skills in writing Python code, as well as an understanding of its algorithms and data structures.