What the course covers.
- Module 1
Machine Learning Foundations
This module introduces the core concepts behind machine learning, including supervised and unsupervised learning, model types, and common use cases. Learners gain a clear understanding of how machine learning systems work and how to approach real-world ML problems effectively.
- Module 2
Building Models with Scikit-Learn
Learners gain hands-on experience building machine learning models using Scikit-Learn. Topics include regression, classification, clustering, feature engineering, and model selection, with a strong focus on practical implementation and clean ML workflows.
- Module 3
Model Evaluation & Optimisation
This module focuses on evaluating and improving model performance. Learners explore metrics, cross-validation, hyperparameter tuning, and techniques to reduce overfitting, ensuring models are reliable, accurate, and production-ready.

