Courses
Structured, self-paced learning paths for data science, machine learning, and AI.
Beginner
Introduction to Data Science
A practical introduction to data science for working professionals. You'll go from raw data to real insights using Python, pandas, and real-world datasets — no academic fluff.
Data Visualisation and Communication
Go beyond basic charts. Learn how matplotlib and seaborn actually work, then use that foundation to build distribution plots, correlation matrices, time series charts, and presentation-ready figures — and develop the judgment to communicate findings clearly to any audience.
Statistics for Data Science
The statistical foundations every data scientist needs — distributions, hypothesis testing, correlation, and inference — taught through the lens of directing and auditing AI-generated analyses.
Exploratory Data Analysis
Learn to direct and validate AI-assisted exploratory data analysis. You'll develop the judgment to know which questions to ask, how to read results critically, and how to catch AI output that looks right but isn't — covering profiling, distributions, outliers, relationships, and how to communicate findings.
AI-Assisted Data Science
A practical credential for data scientists working alongside AI coding tools. You'll learn how AI tools fail in data contexts, how to prompt them effectively, how to audit their output, and how to take professional responsibility for AI-generated analysis.
Practitioner
Data Wrangling & Feature Engineering
Real-world data rarely arrives clean. This course teaches you how practitioners actually prepare data: auditing a new dataset before touching it, resolving missing values strategically, reshaping messy structures, and encoding features ready for a model. You will leave with a repeatable audit and cleaning workflow you can apply to any dataset — and the judgment to know when AI-generated cleaning code is correct and when it isn't.
Feature Engineering & Pipelines
Preprocessing mistakes are the most common cause of models that look good in development and fail in production. This course covers the three forms of data leakage, shows you how sklearn Pipelines make correct preprocessing structural, and teaches you to compose ColumnTransformers for mixed-type DataFrames. You will finish with a production-grade pipeline you can serialise, deploy, and audit — including a systematic protocol for reviewing AI-generated preprocessing code.
Introduction to Machine Learning
A practical introduction to machine learning for working professionals.
Applied Machine Learning
Build production-ready ML workflows: rigorous evaluation, feature selection, ensemble methods, calibration, model interpretability, and end-to-end pipelines that hold up outside the notebook.
Natural Language Processing with Python
Understand how text becomes data: tokenisation, representations, embeddings, and evaluation. Built around the judgment skills practitioners need to direct AI tools confidently and catch silent NLP failures.
Unsupervised Learning & Clustering
Master clustering, dimensionality reduction, and anomaly detection: k-means, DBSCAN, hierarchical methods, PCA, t-SNE, UMAP, Isolation Forest, and PCA reconstruction error. Learn what each algorithm assumes, when to use it, how to choose hyperparameters, and how to evaluate results without ground-truth labels. Build clustering pipelines that actually work in production.
Advanced
Building AI Applications with LLMs
For ML engineers who have deployed models and want to go beyond API integration. This course covers prompt architecture as a typed contract, three-layer output validation, eval frameworks, cost and latency optimisation, observability, and the governance obligations that come with systems that affect real decisions.
Deep Learning with PyTorch
Build, train, and debug neural networks with PyTorch: tensors, autograd, training loops, CNNs, transfer learning, and the systematic skills to catch what AI gets wrong in deep learning code.
Transformer Models & Fine-Tuning
Understand how transformers work at the architectural level, then fine-tune pretrained models from Hugging Face for real tasks — covering attention, tokenisation, full and parameter-efficient fine-tuning, and the systematic skills to catch what AI gets wrong in fine-tuning pipelines.
MLOps & Model Deployment
Close the gap between a working model and a reliable production system. Learn how to package, deploy, monitor, and govern ML models — and develop the judgment to catch the silent failures that AI tools introduce at every stage of the deployment pipeline.
Reinforcement Learning
Master RL from tabular methods to deep policy optimisation. Build working agents in PyTorch, understand when each algorithm class applies, and develop the judgment to audit AI-generated RL configurations before they reach production.