This module marks the turn from describing data to predicting with it. You will build a working vocabulary for machine learning and artificial intelligence, learn how the major families of algorithms differ, and see where ML genuinely helps a business — and where it does not. The second half of the module is about everything that happens before you fit a model: framing the question, checking whether the data can answer it, splitting data honestly, and thinking through the ethical stakes.
Learning Objectives
By the end of this module, you will:
- Define artificial intelligence and machine learning and explain how they relate to data mining
- Distinguish supervised from unsupervised learning, and recognize reinforcement learning and generative AI
- Match specific business problems to the appropriate ML approach
- Frame an ML problem with a clear business question and explicit success criteria
- Assess data readiness and use train/test splits to keep evaluation honest
- Identify data leakage and explain why “future” information invalidates results
- Recognize fairness, privacy, and interpretability considerations in ML applications
Module Resources
Chapters & Notebooks
| 19 |
Introduction to Machine Learning and AI |
— |
| 20 |
Before You Build: Key Considerations |
— |
Lab
| 🧪 Lab 8: Introduction to Machine Learning |
 |