Syllabus

The AI engineering curriculum

Six months full-time, eight months part-time. Five modules that run in order, 15 topics, and reviewed work at the end of each one. Modules I and II are open to read in full.

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Module I

Fundamentals

Weeks 1–5 · Open

Core PythonVariables, control flow, collections, comprehensions, classes and modules — written the way production code is written, not the way tutorials write it.

Python for Data ScienceNumPy and Pandas in depth: dataframes, joins, groupbys, descriptive statistics, distributions, and visualisation that actually answers a question.

SQLQuery, aggregate and window over relational data. Schema design, indexes, and the query patterns that come up in every analytics interview.

Excel & Power BITurn a messy sheet into a dashboard a stakeholder can read. Modelling, DAX basics, and the reporting layer most AI teams still run on.

Module II

Supervised Learning

Weeks 6–11 · Open

Model PreparationCleaning, encoding, scaling, leakage, train/test discipline and cross-validation. The unglamorous half that decides whether a model works.

ClassificationLogistic regression, decision trees, gradient boosting. Precision, recall, ROC-AUC, and choosing a threshold for a business, not a leaderboard.

RegressionPredicting continuous outcomes, regularisation, residual analysis, and reading what a coefficient really claims.

Similarity ModelsDistance metrics, k-nearest neighbours and the recommendation patterns behind "people also bought".

Random ForestsBagging, ensembles and feature importance — why a forest beats a tree and where it still falls over.

Support Vector MachinesMargins, kernels and high-dimensional data. When SVMs remain the sharper tool over a neural network.

Module III

Specialisations

Weeks 16–22

Big DataDistributed processing with Spark, partitioning, and designing pipelines that survive a dataset larger than memory.

Time SeriesTrend, seasonality, stationarity, ARIMA and modern forecasting — plus backtesting that does not lie to you.

Natural Language ProcessingTokenisation, embeddings, transformers, retrieval-augmented generation and evaluating a system whose output is text.

TensorFlow & KerasBuilding, training and serving deep models — plus the deployment step most courses skip entirely.

Deep LearningCNNs, RNNs, attention, fine-tuning and agentic workflows that chain models, tools and memory together.

Modules III–VII

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