AI Engineering & Machine Learning Advanced Professional Diploma

NumPy/pandas, vectors/matrices concepts, probability/statistics refresh, data quality and experiment thinking.

Practical work: Complete a diagnostic notebook and data-preparation exercise.

Supervised/unsupervised learning, preprocessing, feature engineering, model selection and metrics.

Practical work: Train and compare multiple baseline models.

Neural networks, training workflow, regularization, computer vision/NLP foundations and practical frameworks.

Practical work: Build one image or text classification project.

Embeddings, prompting, retrieval-augmented generation, vector search, tool use, evaluation and safety.

Practical work: Build a document-grounded assistant using a small approved knowledge base.

APIs, model packaging, experiment tracking concepts, monitoring, versioning, containers and cloud deployment concepts.

Practical work: Serve a model through an API and document deployment steps.

Pipelines, data validation, storage choices, batch vs streaming concepts, governance and lineage basics.

Practical work: Design a reproducible data-to-model pipeline.

Bias/fairness, privacy, security, explainability, red teaming, hallucination evaluation and governance.

Practical work: Create an evaluation scorecard and risk register for an AI use case.

Problem selection, data, model/application build, evaluation, deployment, documentation and demo.

Practical work: Deliver a production-style AI capstone with repo, model card and presentation.