Machine Learning (2026-2027)

The 2026-2027 practical track for Machine Learning, built on the current course plan: advanced pandas and reading data from many sources, visualization with Matplotlib and Seaborn, linear and logistic regression, k-nearest neighbors, decision trees and random forests, naive Bayes, k-means clustering, content-based recommendations, sentiment analysis, and the final project. The labs run real Python code on real datasets, and each notebook has a link to open it in Colab.