AI Engineering Curriculum

Ship 16 production AI systems.
From foundations to agentic AI.

An intensive cohort for software engineers ready to put AI in production. Live sessions, hands-on labs, real shipped work — every phase ends in something on your portfolio.

16 phases 16 capstones 314 lessons linked 16 tracks covered Built for engineers, not students

Dig deeper

Full syllabus, tools, and a guided demo.

Phases:

The Curriculum Spine

Nine phases. Nine capstones. One production-grade portfolio.

Each phase ends in shipped work — not a quiz. Topic chips link directly to the lesson where the concept is taught.

Phase 1 of 16

Orientation

What AI is · How this course works

Capstone you ship

Your first working model, start to finish

16 topics · linked to lessons

Welcome to AI AI History & Future AI Solving Real Problems What Is the Software Industry?The Software Industry Tech Roles and Career Paths How Software Gets Built Every Document You'll Meet From Idea to Product Tools of the Trade A Day and a Sprint in the Life Engineering Practices Corporate Processes Project and Delivery Management The Technology Landscape The Career Playbook

Phase 2 of 16

Python Engineering

Syntax · Data structures · Internals · Testing

Capstone you ship

A packaged, tested Python tool

46 topics · linked to lessons

Python Visual Tour Your First Python Program Variables, Types & Operators Mini-Project Control Flow Functions Lists, Tuples, Dicts & Sets List Comprehensions Dictionaries & Hash Tables Strings Error Handling & Debugging File I/O & Exception Handling Modules, Packages & pip Environment Variables & Secrets Calling APIs in Python The collections Module Regular Expressions NumPy & Pandas Pandas Advanced Data Visualization with Matplotlib Python for Machine Learning Databases with Python Type Hints, Testing & Code Quality Classes & Object-Oriented Programming Advanced OOP, Part 1 Advanced OOP, Part 2 Python's Data Model Decorators, Generators & Context Managers Python's Memory Model Python Internals Async/Await & Concurrency Essential Algorithms Recursion & Dynamic Programming Linked Lists, Part 1 Linked Lists, Part 2 Stacks, Queues & Deque Trees & BST, Part 1 Trees & BST, Part 2 Heaps, Priority Queues & Graphs Testing with pytest Building APIs with FastAPI Reading Real Python Code Production Python Python Mini-Projects, Part 1 Python Mini-Projects, Part 2 Capstone

Phase 3 of 16

SQL & Data Access

Querying · Joins · Modelling · Indexes

Capstone you ship

A query suite over a normalised schema

13 topics · linked to lessons

Your First SQL Query Filtering & Sorting Data Aggregate Functions & GROUP BY JOINs -- Combining Tables Subqueries & CTEs Window Functions INSERT, UPDATE, DELETE CREATE TABLE Indexes & Performance Data Modeling Advanced SQL SQL for Data Science SQL Interview Patterns

Phase 4 of 16

Math for ML

Linear algebra · Calculus · Probability

Capstone you ship

Gradient descent implemented from scratch

17 topics · linked to lessons

Vectors & Spaces Matrices as Transformations Tensor Notation, Einsum & Computational Graphs Eigenvalues & SVD Calculus Vector & Matrix Calculus Probability & Bayes' Theorem Descriptive Statistics & Distributions Random Variables, Expectation & Variance Multivariate Gaussian & Joint Distributions Statistical Inference MLE, MAP & Fisher Information Markov Chains & MDPs Optimization & Gradient Descent Convex Optimization, KKT & Lagrangian Duality Information Theory & Entropy Stochastic Calculus

Phase 5 of 16

Data & Features

Cleaning · Leakage · Feature engineering

Capstone you ship

An end-to-end EDA and feature pipeline

15 topics · linked to lessons

The Data Pipeline Data Sources & Ingestion Exploratory Data Analysis Data Quality & Cleaning Outlier Detection Categorical Encoding Feature Engineering Feature Selection Dimensionality Reduction Time Series Data Handling Imbalanced Datasets Data Augmentation Train/Val/Test & Data Leakage Cross-Validation Strategies Data Drift & Validation

Phase 6 of 16

Classical ML

Regression · Trees · Ensembles · Evaluation

Capstone you ship

A tuned classifier, honestly evaluated

Phase 7 of 16

Deep Learning

Autograd · CNNs · Architectures · Training at scale

Capstone you ship

A network trained with a real training loop

Phase 8 of 16

NLP & Transformers

Tokenisation · Attention · BERT · GPT · Fine-tuning

Capstone you ship

A fine-tuned LLM with a defensible evaluation

Phase 9 of 16

Generative AI

VAEs · GANs · Diffusion · Video & audio

Capstone you ship

A diffusion model you can prompt

19 topics · linked to lessons

Generative Models Landscape & Evaluation Variational Autoencoders VQ-VAE & Discrete Latents Generative Adversarial Networks Normalizing Flows Score-Based Generative Models Diffusion Models (DDPM, DDIM, CFG)Flow Matching & Beyond Latent Diffusion & Stable Diffusion Conditional & Controllable Generation Consistency Models & Distillation Video Generation 3D Generation Audio Generative AI Evaluation Deep Dive Safety, Copyright & Watermarking Modern GenAI Landscape Building a Diffusion App Diffusion DPO & RLHF

Phase 10 of 16

Reinforcement Learning

MDPs · Q-learning · Policy gradients · RLHF

Capstone you ship

An agent that learns by playing

18 topics · linked to lessons

RL Foundations Dynamic Programming Exploration vs Exploitation Monte Carlo & TD Learning Q-Learning Deep Q-Networks (DQN)Policy Gradient Methods Actor-Critic Methods Proximal Policy Optimization SAC & TD3 Imitation Learning Model-Based RL & MCTS Search + Learning Multi-Agent RL Offline / Batch RL The RL to RLHF Bridge Hierarchical RL & Options AlphaZero & MuZero

Phase 11 of 16

RAG Systems

Chunking · Retrieval · Reranking · Evaluation

Capstone you ship

A retrieval pipeline that cites its sources

16 topics · linked to lessons

Why RAG?Embeddings & Vector Similarity The RAG Pipeline Vector Databases & Indexing Chunking Strategies Retrieval Algorithms ANN Search at Scale Re-ranking Query Rewriting & Advanced Retrieval Agentic RAG Knowledge Graphs & Graph RAG Multimodal RAG RAG Evaluation Long Context vs RAG RAG Security & Production Deployment Building a RAG App

Phase 12 of 16

Agentic AI

Tools · MCP · ReAct · Multi-agent orchestration

Capstone you ship

An agent that completes real work unattended

17 topics · linked to lessons

Agent Fundamentals Tool Use & Function Calling Function Calling MCP ReAct Agent Memory Systems Reflexion & Self-Critique Planning & Reasoning Agentic Workflow Patterns Agent Frameworks Multi-Agent Systems Computer Use & GUI Agents Code Generation Agents Agent Evaluation Agent Safety & Guardrails Production Deployment & Observability Building an Agent (Capstone)

Phase 13 of 16

AI-Assisted Engineering

Working with coding agents in a real repository

Capstone you ship

A feature shipped with an agent as pair

17 topics · linked to lessons

Getting Started The Claude Code Loop Prompt Patterns & Interaction CLAUDE.md The .claude/ Ecosystem Permissions & Safety Slash Commands & Skills Hooks Subagents & Delegation MCP Code Generation & Feature Building Debugging & Fixing with Claude Code Git Workflows & Worktrees Code Review Web & External Data Background Tasks & Monitoring Teams, Cost & Scale

Phase 14 of 16

ML Engineering & Production

Serving · Monitoring · Cost · Compliance

Capstone you ship

A deployed, monitored, budgeted service

Phase 15 of 16

Forward-Deployed Engineering

Discovery · Scoping · Delivery with customers

Capstone you ship

A scoped solution with a costed plan

12 topics · linked to lessons

The Engineer Who Ships Where the Customer Lives The Engagement Lifecycle Discovery Scoping Prototyping Data Integration in the Wild Deployment POC to Production Running the Pilot Feeding the Product Stakeholders Becoming an FDE

Phase 16 of 16

Career & Placement

Portfolio · Interviews · Offers

Capstone you ship

An interview-ready portfolio and profile

12 topics · linked to lessons

Build Your First End-to-End AI App Build a Chatbot in 50 Lines Building an AI Portfolio That Gets Interviews The 90-Day Roadmap How Top AI Companies Actually Structure Interviews The AI Engineer Resume That Gets Interviews Adding AI to Your Existing Tech Stack Learn AI Without Quitting Your Job How To Practice AI Interviews Systematically ML Production Disasters Interview Anti-Patterns AI Safety as a Career

Ship 16 production AI systems.From foundations to agentic AI.

16 capstones. Real systems on your portfolio.

Your first working model, start to finish

A packaged, tested Python tool

A query suite over a normalised schema

Gradient descent implemented from scratch

An end-to-end EDA and feature pipeline

A tuned classifier, honestly evaluated

A network trained with a real training loop

A fine-tuned LLM with a defensible evaluation

A diffusion model you can prompt

An agent that learns by playing

A retrieval pipeline that cites its sources

An agent that completes real work unattended

A feature shipped with an agent as pair

A deployed, monitored, budgeted service

A scoped solution with a costed plan

An interview-ready portfolio and profile

The four things you actually want to know.

Who it's for

Prerequisites

Format

What you leave with

Full syllabus, tools, and a guided demo.

Nine phases. Nine capstones. One production-grade portfolio.

Orientation

Python Engineering

SQL & Data Access

Math for ML

Data & Features

Classical ML

Deep Learning

NLP & Transformers

Generative AI

Reinforcement Learning

RAG Systems

Agentic AI

AI-Assisted Engineering

ML Engineering & Production

Forward-Deployed Engineering

Career & Placement

Stop watching tutorials. Ship something.

Ship 16 production AI systems.From foundations to agentic AI.

16 capstones. Real systems on your portfolio.

Your first working model, start to finish

A packaged, tested Python tool

A query suite over a normalised schema

Gradient descent implemented from scratch

An end-to-end EDA and feature pipeline

A tuned classifier, honestly evaluated

A network trained with a real training loop

A fine-tuned LLM with a defensible evaluation

A diffusion model you can prompt

An agent that learns by playing

A retrieval pipeline that cites its sources

An agent that completes real work unattended

A feature shipped with an agent as pair

A deployed, monitored, budgeted service

A scoped solution with a costed plan

An interview-ready portfolio and profile

The four things you actually want to know.

Who it's for

Prerequisites

Format

What you leave with

Full syllabus, tools, and a guided demo.

Nine phases. Nine capstones. One production-grade portfolio.

Orientation

Python Engineering

SQL & Data Access

Math for ML

Data & Features

Classical ML

Deep Learning

NLP & Transformers

Generative AI

Reinforcement Learning

RAG Systems

Agentic AI

AI-Assisted Engineering

Ship 16 production AI systems.
From foundations to agentic AI.

Ship 16 production AI systems.
From foundations to agentic AI.