AI / Machine Learning

Lessons

• 01Python environment setup — conda, venv & dependency management
• 02NumPy — vectorised operations, broadcasting & performance
• 03Pandas — data ingestion, cleaning, transformation & aggregation
• 04Exploratory data analysis (EDA) — finding patterns before modelling
• 05Data visualisation — Matplotlib, Seaborn & Plotly
• 06Feature engineering & selection — creating predictive signals
• 07Scikit-learn pipelines — preprocessing, transformers & estimators
• 08Classification & regression — training, tuning & validation
• 09Model evaluation — cross-validation, AUC-ROC, RMSE & confusion matrix
• 10Deploying ML models as REST APIs with FastAPI

After this module you can

• Build end-to-end ML pipelines
• Conduct rigorous exploratory data analysis
• Deploy trained models as production APIs