Machine Learning

Goal

The purpose of this course is to introduce fundamental principles, algorithms and metrics for Machine Learning. It includes examples and exercises that help you train your own models in Python.

Fundamentals

• What is Machine Learning?
• Training Models
• Python Libraries for Machine Learning

Unsupervised Learning

• Distance Metrics
• Clustering
• Hierarchical Clustering
• K-Means Clustering
• DBSCAN

Supervised Learning

• k-Nearest-Neighbors
• Linear Regression
• Gradient Descent
• Feature Engineering
• Column Transformers and Pipelines
• Evaluating Linear Regression Models
• Logistic Regression
• Decision Trees
• Random Forests

Evaluating Models

• Evaluating Classifiers
• Cross-Validation

Appendix

• The Project
• Further Reading

License

© 2024 Dr. Kristian Rother and everybody in AUTHORS.md.

Usable under the conditions of the Creative Commons Attribution Share-alike License 4.0 (CC-BY-SA 4.0). See creativecommons.org for details