Machine Learning
Building models that learn from data. Classical ML algorithms form the foundation of all AI systems — understand them deeply before moving to deep learning. Scikit-learn is the industry-standard library. Model evaluation determines whether your model actually works.
Intermediate
Scikit-learn
The go-to Python ML library. Classification, regression, clustering, and dimensionality reduction with a clean API.
Intermediate
Model Evaluation
Accuracy, precision, recall, F1, ROC curves, cross-validation, and the techniques that separate real models from overfit ones.
Intermediate
Feature Engineering
Transforming raw data into the inputs that make models actually learn. Encoding, scaling, selection, and creation.
Intermediate
ML Deployment
Serving trained models as APIs. FastAPI, model serialisation, versioning, and keeping models alive in production.