| 1 | Introduction to machine learning and fundamental concepts | [1]p.1–10 |
| 2 | Data preprocessing techniques and feature engineering | [1]p.11–22 |
| 3 | Supervised learning: regression methods | [2]p.5–18 |
| 4 | Supervised learning: classification algorithms | [1]p23–35 |
| 5 | Unsupervised learning: clustering approaches | [2]p.19–31 |
| 6 | Unsupervised learning: dimensionality reduction methods | [1]p.36–48 |
| 7 | Model evaluation and selection metrics | [1]p.49–60 |
| 8 | Introduction to deep learning concepts | [3]p.3–12 |
| 9 | Real-world applications: classification scenarios | [2]p.32–42 |
| 10 | Model optimization and hyperparameter tuning | [1]p.61–73 |
| 11 | Industrial applications of machine learning | [3]p.13–25 |
| 12 | Ethics and security in machine learning | [2]p.43–50 |
| 13 | Project development process | [1]p.74–85 |
| 14 | Project presentation and general evaluation | |