Machine learning combines data, algorithms and computing to build systems that learn patterns and make predictions or decisions.

Foundations

Learn Python, basic statistics, probability, linear algebra concepts and data manipulation.

Core machine learning

Study supervised and unsupervised learning, regression, classification, clustering, feature engineering and model evaluation.

Practical projects

Work with real datasets and practise data cleaning, train-validation-test splits, model selection and error analysis.

Deployment and MLOps

Learn APIs, containers, cloud platforms, monitoring, model versioning and reproducible pipelines.