Mastering Supervised Learning

At a Glance

This is an immersive course designed to provide participants with practical experience in supervised learning algorithms and techniques. Participants will gain hands-on experience in building, training, and evaluating supervised machine learning models using popular Python libraries such as scikit-learn.

Objectives

By the end of the course, participants will be equipped to:

• Understand the principles of supervised learning and its applications in real-world problems
• Implement various supervised learning algorithms
• Preprocess and prepare data for training supervised learning models
• Evaluate and interpret model performance using appropriate metrics and techniques
• Apply best practices for model selection, tuning, and validation in supervised learning projects

Prerequisite

Participants should have a basic understanding of Python programming and familiarity with fundamental concepts in machine learning. Prior exposure to data preprocessing, model evaluation, and basic statistical concepts is recommended but not required.