Resource Focus
Workflow & data preparation
Revisit the ML lifecycle, preprocessing boundaries, leakage prevention and reproducible pipelines.
Algorithm families
Use the chapter map to navigate regression, classification, ensembles, kernel/probabilistic methods and unsupervised learning.
Evaluation & improvement
Return to cross-validation, thresholding, tuning, inspection and model-selection resources.
Operational ML
Connect deployment, monitoring and MLOps concepts with the evaluation discipline taught earlier in the module.
Chapter Resource Map
Every chapter in this Module already contains its own Key Terms and References & Further Reading collections. These links open the relevant chapter and automatically expand the requested resource section.
Foundations, Lifecycle & Preparation
Chapter 1 — Machine Learning Foundations
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 2 — The Machine Learning Lifecycle
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 3 — Preparing Data for Machine Learning
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Supervised & Unsupervised Methods
Chapter 4 — Regression
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 5 — Classification
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 6 — Decision Trees and Ensemble Learning
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 7 — Distance-Based, Probabilistic and Kernel Methods
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 8 — Clustering and Unsupervised Learning
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 9 — Dimensionality Reduction and Anomaly Detection
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Features, Evaluation & Operations
Chapter 10 — Feature Engineering and Imbalanced Data
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 11 — Model Evaluation, Validation and Improvement
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 12 — Deployment, Monitoring and Machine Learning Operations
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Curated External Reference Shelf
This shelf surfaces a small set of high-value references already used by the Module. The complete chapter-specific source lists remain inside the individual chapters.
| Resource | Why it is useful | Used in |
|---|---|---|
| scikit-learn User Guide | Primary implementation reference across the module | Chapters 1–12 |
| scikit-learn — Common Pitfalls and Recommended Practices | Leakage, preprocessing and evaluation pitfalls | Chapters 1–12 |
| Model selection and evaluation | Metrics, model selection and evaluation workflows | Chapters 2, 11 |
| Preprocessing data | Scaling, encoding and preprocessing primitives | Chapter 3 |
| Pipelines and composite estimators | Leakage-safe workflow composition | Chapter 3 |
| Ensemble methods | Trees, forests, boosting and ensemble methods | Chapter 6 |
| Feature selection | Feature-selection methods and implementation reference | Chapter 10 |
| Cross-validation | Validation strategies and implementation guidance | Chapter 11 |
| Inspection | Model inspection and interpretability tooling | Chapter 11 |
Continue the Learning Journey
Return to Machine Learning
Continue the full 12-Chapter learning sequence rather than using the resource directory as a substitute for the lessons.
Open the Module →Next: Neural Networks
Continue into the next logical learning area when you are ready.
Continue Learning →NIYAMA-VIDYĀ Resources
Return to the permanent site-wide directory for Learn, Explore, Applied AI and transparency resources.
Back to Resources →Reference Transparency
External documentation and publications remain the property of their respective owners and are provided for attribution, verification and further learning. See Third-Party Rights & Attribution and the Website Disclaimer for the site-wide transparency framework.