Machine Learning (scikit-learn)
The planned outline of 15 modules. Lessons are written, run and reviewed before they are published.
Before this track
Planned outline
- Module 1
Machine learning foundations and the scikit-learn API
Coming soon - Module 2
Generalisation: splits, overfitting and baselines
Coming soon - Module 3
Regression
Coming soon - Module 4
Classification
Coming soon - Module 5
Model evaluation and selection
Coming soon - Module 6
Preprocessing and feature engineering
Coming soon - Module 7
Pipelines and composite estimators
Coming soon - Module 8
Classic models: neighbours, Bayes, discriminants, SVMs and MLPs
Coming soon - Module 9
Decision trees and ensembles
Coming soon - Module 10
Clustering and anomaly detection
Coming soon - Module 11
Dimensionality reduction
Coming soon - Module 12
Interpretation and responsible ML
Coming soon - Module 13
Special problems: imbalance, few labels, recommenders and forecasts
Coming soon - Module 14
Scale, persistence and deployment basics
Coming soon - Module 15
GATE DA practice, interviews and projects
India-specific Coming soon
Official documentation
- scikit-learn.org/stable/user_guide.html (scikit-learn.org)
- developers.google.com/machine-learning/crash-course (developers.google.com)
- gate2027.iitm.ac.in/static/doc/GATE2027_Syllabus/DA_GATE2027_Syllabus.pdf (gate2027.iitm.ac.in)
More in AI and machine learning
- AI Literacy & Prompting (coming soon)
- Building LLM Applications (coming soon)
- Artificial Intelligence (classical AI and soft computing) (coming soon)
- Mathematics for Machine Learning (coming soon)
- All AI & ML tracks
- How we make lessons