Machine Learning — Practical Foundations

About this course

Build modern machine learning capability through structured lessons, interactive labs, projects, and verified portfolio evidence.

What you will be able to do

• Apply core machine learning concepts
• Complete practical scenario work
• Explain decisions and tradeoffs
• Publish verified project evidence

Prerequisites

Basic computer literacy and willingness to complete practical work.

Course curriculum

1. Problem framing and responsible ML 20 minutes
2. Data preparation and feature quality 20 minutes
3. Regression and classification 20 minutes
4. Training, validation, and leakage prevention 20 minutes
5. Evaluation, fairness, and explainability 20 minutes
6. Deployment monitoring and iteration 20 minutes

Interactive labs

• ML Data Split & Leakage Lab 25 minutes
• ML Classification Lab 30 minutes
• ML Evaluation & Fairness Lab 30 minutes