Machine Learning

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

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

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

Supervised vs Unsupervised Learning

Linear & Logistic Regression

Bias-Variance Tradeoff

Cross-Validation

Feature Scaling & Encoding

Decision Trees

Random Forests

Support Vector Machines

Gradient Boosting (XGBoost)

K-Nearest Neighbors

Pandas for Data Manipulation

Feature Engineering

Handling Missing Data

Dealing with Imbalanced Data

Data Visualisation with Matplotlib

Confusion Matrix & Metrics

ROC Curves & AUC

Precision vs Recall

Hyperparameter Tuning

A/B Testing for ML

Perceptrons & Activation Functions

Backpropagation

Building with PyTorch

Training & Optimisation

Regularisation & Dropout

Convolutional Neural Networks

Recurrent Neural Networks

Transfer Learning

Hugging Face Transformers

Practical Deep Learning

Model Versioning

MLflow Tracking

Model Deployment

Monitoring & Drift Detection

CI/CD for ML

Feature Stores

Model Serving

Scaling ML Systems

Cost Optimisation

End-to-End ML Platform

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

The Recommendation Problem

Collaborative Filtering: Neighbours and Similarity

Matrix Factorization

Ranking, Metrics and Offline Evaluation

Content, Hybrids and Cold Start

Recommenders in Production