6 Months
Structured career sprint
September 2026 Intake Open
AI & Machine Learning Engineer
Move from machine learning foundations to deployable intelligent systems in six months, including deep learning, LLMs, RAG, AI agents, evaluation and MLOps.
Trusted by Experts
Taught by Senior Professionals
The Career
What does an AI & Machine Learning Engineer do?
An AI & Machine Learning Engineer turns data and models into dependable intelligent products. They frame real problems, build and evaluate machine learning pipelines, create retrieval and agentic systems, add safeguards and deploy monitored services that people can use responsibly.
- Production ML pipelines
- Deep learning foundations
- RAG and AI agents
- Evaluation, APIs and MLOps
Built Around Real Career Outcomes
100% Online
Flexible guided learning
Verified Work
Portfolio-ready evidence
Expert Mentors
Professional feedback
Course Breakdown
Your Complete Learning Path
A practical progression from foundations to a portfolio-ready industrial project, with feedback throughout the journey.
01Linear Algebra & Probability for ML
Work with vectors, matrices, tensors, mean, variance and probability distributions.
02Data Manipulation & Exploratory Analysis
Use Pandas for data cleaning, missing values, exploration and visualisation.
03Supervised Learning Pipelines
Build regression and classification workflows while understanding overfitting and bias-variance trade-offs.
04Tree-Based Models & Feature Engineering
Use decision trees, Random Forest, XGBoost, encoding, scaling and Scikit-Learn pipelines.
05Model Evaluation & Business Framing
Evaluate precision, recall, F1, ROC-AUC and the real business impact of a model.
06Neural Networks & Deep Learning
Learn perceptrons, activation functions, backpropagation and PyTorch or TensorFlow.
07Transformers & LLM Foundations
Understand tokenisation, embeddings, attention mechanisms and Transformer architecture.
08Prompt Engineering & Retrieval-Augmented Generation
Work with context windows, reliable prompting, RAG, Pinecone and ChromaDB.
09Building AI Agents
Use OpenAI agent tooling, LangChain, tool use and multi-step AI tasks.
10Model Context Protocol & Multi-Agent Systems
Build with MCP, tool calling, shared memory, delegation and multi-agent workflows.
11Evaluation, Guardrails & Observability
Trace decisions, evaluate LLM systems, reduce hallucinations and use tools safely.
12APIs & Containerisation
Serve systems with FastAPI and REST, then package them with Docker.
13MLOps Fundamentals
Use MLflow, experiment tracking, model registries, CI/CD and AWS or GCP deployment.
14End-to-End Intelligent System
Build, evaluate and deploy a complete solution combining predictive ML with an agentic AI interface.
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Want to make sure which path fits you?
Let us walk you through the curriculum, career outcomes, and the exact skills you’ll build so you can decide with clarity. Whether you’re starting fresh or leveling up, we’ll help you choose the track that matches your goals.