AI / Machine Learning
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.
11 lessons
Start courseLessons
- 1Advanced Pandas and Reading Data from Many Sources27 min read
- 2Data Visualization with Matplotlib and Seaborn21 min read
- 3Linear Regression20 min read
- 4Logistic Regression21 min read
- 5K-Nearest Neighbors Classification16 min read
- 6Decision Trees and Random Forest22 min read
- 7Naive Bayes Classification10 min read
- 8k-Means Clustering23 min read
- 9Content-Based Recommendations19 min read
- 10Sentiment Analysis with Binary Classification6 min read
- 11Final Project Submission11 min read
