The course moves from foundations to applied business systems¶
Use this roadmap to understand how the chapters build on one another and to identify the background knowledge that will help you move efficiently through the material.
Learning Sequence¶
Major Stages¶
| Stage | Focus | Outcome |
|---|---|---|
| Foundations | Business framing, notation, and prerequisites | Understand the language and structure of ML problems |
| Metrics and evaluation | Error measures, classification metrics, and business reporting | Judge models using appropriate evidence |
| Supervised and unsupervised models | Regression, classification, clustering, and recommendations | Build practical solutions for common business cases |
| Forecasting and advanced AI | Time series, survival analysis, deep learning, and LLM topics | Extend beyond standard tabular prediction |
| Production and capstones | Monitoring, deployment, and project synthesis | Turn technical work into sustainable business value |
Prerequisites¶
| Skill or tool | Why it helps |
|---|---|
| Basic Python | Needed to read, run, and modify notebook examples |
| Introductory statistics | Helps with distributions, uncertainty, and evaluation |
| Comfort with tables and charts | Supports interpretation of model outputs |
| Curiosity about business decisions | Keeps the technical work tied to a meaningful goal |
Self-Check¶
Before moving on, note whether you are already comfortable with Python syntax, basic spreadsheet-style analysis, and reading tables or plots. If any of these still feel unfamiliar, slow down in the opening chapters and use the practice notebooks more heavily.
Continue¶
You are ready to move into the foundations section and start with the first conceptual chapters in the recommended order shown in the sidebar.