From linear regression to neural networks. Build models that learn from data.
Never written code? You can still start here.
Module 0 opens with 18 lessons of programming from nothing — your first program, variables, loops, functions, reading errors, the terminal, Git and arrays — before any of the Machine Learning material. No prior coding assumed.
AI Foundations — optional basics (free)
Week 0 · 20 lessons
Beyond the chatbox: what AI assistants really are
How the model "thinks" — and why it changes how you ask
The capability map: what AI does brilliantly
The limits: hallucination, freshness, and what never to trust it with
Your first real win: turn a 30-minute task into 3
6 senior modules · 34lessons · capstone & certificate. Upgrade your plan to unlock the advanced tier.
Bayesian Machine Learning
5 lessons
Bayesian Probability Foundations for ML
Markov Chain Monte Carlo
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House Price Predictor
Email Spam Classifier
Customer Segmentation Dashboard
About
Machine Learning
20 questions · ~30 minutes
Context is everything: give it what it needs to know
Give it a role and a goal
Constraints and format: define what "good" looks like
Show, don't tell: examples and templates
Iterate like a pro: steer, don't restart
Documents and reports: summarise, extract, and draft better
Spreadsheets and data: Excel formulas, analysis, and charts
Presentations: from blank page to a solid deck
Long, messy input: transcripts, threads, notes, and screenshots
ChatGPT, Claude, Copilot, Gemini: which to reach for, and when
Make smarter decisions: AI as your thinking partner
Chaining steps: multi-part tasks done reliably
Your prompt library: reusable templates that save hours
Staying safe: privacy, accuracy, and company data
Capstone: do a real task end-to-end (graded) → your certificate
Module 0 — Foundations: Math & Tooling for ML
Week 0 · 23 lessons
What a program is and how to run your first one
Storing values in variables, and why text and numbers behave differently
Making decisions with if, else and comparisons
Storing many values in a list
Looking things up by name with dictionaries
Doing something to every item with loops
Writing your own functions
Reading errors without panic
Finding the mistake in your own code
Saving your code in a file and running it
Importing code other people have written
Giving each project its own environment
Talking to your computer with the terminal
Saving your work with Git
Putting your work on GitHub
What an array is, and why a list is not enough
Picking out pieces: indexing and slicing, including two dimensions
Maths on a whole array at once, and where you go next
Linear Algebra for ML: Vectors, Matrices & the Dot Product
Calculus & Gradients: How Models Learn
Probability & Statistics for ML
Your First Statistical Model: Linear Regression
Your ML Toolkit: NumPy & Git
ML Foundations
Week 1 · 5 lessons
Supervised vs Unsupervised Learning
Linear & Logistic Regression
Bias-Variance Tradeoff
Cross-Validation
Feature Scaling & Encoding
Classical ML Algorithms
Week 2 · 5 lessons
Decision Trees
Random Forests
Support Vector Machines
Gradient Boosting (XGBoost)
K-Nearest Neighbors
Data Pipelines
Week 3 · 5 lessons
Pandas for Data Manipulation
Feature Engineering
Handling Missing Data
Dealing with Imbalanced Data
Data Visualisation with Matplotlib
Model Evaluation
Week 4 · 5 lessons
Confusion Matrix & Metrics
ROC Curves & AUC
Precision vs Recall
Hyperparameter Tuning
A/B Testing for ML
Neural Networks
Week 5 · 5 lessons
Perceptrons & Activation Functions
Backpropagation
Building with PyTorch
Training & Optimisation
Regularisation & Dropout
Deep Learning
Week 6 · 5 lessons
Convolutional Neural Networks
Recurrent Neural Networks
Transfer Learning
Hugging Face Transformers
Practical Deep Learning
MLOps
Week 7 · 5 lessons
Model Versioning
MLflow Tracking
Model Deployment
Monitoring & Drift Detection
CI/CD for ML
Production ML
Week 8 · 5 lessons
Feature Stores
Model Serving
Scaling ML Systems
Cost Optimisation
End-to-End ML Platform
Time Series & Forecasting
Week 9 · 6 lessons
Thinking in Time: Components, Stationarity and the Resample Toolkit
Baselines That Beat Fancy Models
Classical Forecasting: Exponential Smoothing and ARIMA
Backtesting Without Lying to Yourself
ML for Forecasting: Features, Trees and Global Models
Forecasting in Production
Recommender Systems
Week 10 · 6 lessons
The Recommendation Problem
Collaborative Filtering: Neighbours and Similarity
Matrix Factorization
Ranking, Metrics and Offline Evaluation
Content, Hybrids and Cold Start
Recommenders in Production
Probabilistic Programming with PyMC
Variational Inference
Uncertainty Quantification & Bayesian Neural Networks
Causal Machine Learning
5 lessons
Causal Graphs & the Do-Calculus
Identification Strategies
Heterogeneous Treatment Effects & Meta-Learners
Uplift Modeling & Policy Learning
Causal ML in Production
Advanced NLP & Transformers
5 lessons
Transformer Architecture Deep Dive
Fine-tuning LLMs
Retrieval-Augmented Generation (RAG)
RLHF & Alignment
LLMs in Production
Production MLOps at Scale
5 lessons
Distributed Training
Feature Stores at Scale
Model Serving & Inference at Scale
ML Observability & Drift Detection
ML Platform Architecture
Responsible AI & ML Interpretability
5 lessons
Model Explainability with SHAP
LIME & Counterfactual Explanations
Fairness in ML
AI Governance & Regulation
Responsible AI in Practice
Capstone & ML Research Frontiers
9 lessons
Scaling Laws & Emergent Abilities
Multimodal Foundation Models
ML System Design for Industry
Reproducibility & Research Methods
Capstone Synthesis & Career Positioning
AutoML & Neural Architecture Search
Graph Neural Networks
Self-Supervised & Foundation Models for Structured Data
Reading ML Research & Staying Current