Syllabus

The AI engineering curriculum

Six months full-time, & upto nine months part-time. Seven modules that run in order, 17 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

AI Engineering Foundations

Weeks 1–5 · Open

Production PythonWrite clean, reusable Python for AI applications. Master functions, data structures, OOP, modules, environments, debugging and the coding patterns used in real projects.

Data Engineering with PythonTurn raw data into something models can use. Work with NumPy and Pandas to clean, transform, join, analyse and visualise real-world datasets.

SQL & Data SystemsQuery and structure the data behind AI products. Learn joins, aggregations, window functions, relational modelling, indexes and efficient analytical queries.

APIs & Developer WorkflowLearn how AI applications connect to real software. Work with REST APIs, JSON, Git, virtual environments and the developer workflow used to build and ship projects.

Analytics for AI TeamsTurn model and business data into decisions. Build clear reports and dashboards while learning the analytics layer used to communicate AI performance and outcomes.

Module II

Applied Machine Learning

Weeks 6–11 · Open

ML Pipelines & Feature EngineeringPrepare real-world data for modelling. Handle missing values, encoding, scaling, feature creation, leakage, train/test splits and cross-validation correctly.

Classification SystemsBuild models that make decisions from labelled data using logistic regression, decision trees and gradient boosting — then evaluate what actually works.

Regression & ForecastingPredict continuous outcomes using linear models and regularisation. Learn residual analysis, error metrics and how to turn predictions into useful decisions.

Similarity Search & RecommendersLearn how machines measure similarity using distance and nearest-neighbour methods — the foundation behind recommendations, semantic search and modern retrieval systems.

Ensemble LearningCombine multiple models to build stronger predictors with Random Forests, bagging and boosting. Understand feature importance, overfitting and model trade-offs.

Model Evaluation & ExplainabilityGo beyond accuracy. Evaluate models with precision, recall, F1, ROC-AUC and business metrics, then understand why a model made a prediction.

Support Vector MachinesUnderstand margins, kernels and high-dimensional decision boundaries — and when classical ML can still outperform a more complex neural approach.

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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