Machine learning, deep learning, NLP and deployment. Python-first, project-based, with a portfolio of 14 working models.
By the end of the programme, you will ship real deliverables.
NumPy, pandas, matplotlib and the full scientific Python stack.
Regression, classification, clustering, and model evaluation.
CNNs, RNNs, transformers — trained on real datasets.
Prompting, fine-tuning, RAG patterns, and responsible deployment.
Model serving, monitoring, and basic MLOps hygiene.
Three deployable projects you can demo and discuss.
Each phase builds on the last. Live instruction with reviewable deliverables.
NumPy, pandas, visualisation, data cleaning, and statistics refresher.
Linear and logistic regression, trees, ensembles, clustering, evaluation metrics.
TensorFlow/PyTorch, CNNs for vision, RNNs for sequences, transfer learning.
Attention, transformers, prompting, fine-tuning, and retrieval-augmented generation.
Model serving, containerisation, monitoring, and responsible AI.
End-to-end project from problem statement to deployed, documented model.
Software engineer wanting practical ML without a research-track detour.
Analyst moving beyond SQL and BI into predictive modelling.
STEM graduate wanting a project portfolio that beats a certificate.
ETL pipelines, Apache Spark, Databricks, Kafka and SQL. A specialised, well-paid track.
Power BI, Tableau, SQL and Python for analysis. Turn data into decisions.
AWS, Azure and GCP preparation. Structured mock tests and concept deep-dives.
A free demo class with the instructor. If it is not a fit, you owe nothing.