AI & Machine Learning Tutorial

Master artificial intelligence and ML — 59 lessons from your first model to advanced LLMs, computer vision, and production deployment.

Learn AI and machine learning from scratch: regression, classification, neural networks and deep learning, then NLP, computer vision and transformers.

What you'll learn

• ✓ Core concepts: how machine learning models learn from data
• ✓ Build classifiers and work with real datasets in Python
• ✓ Neural networks, deep learning and computer vision fundamentals
• ✓ Natural language processing and modern LLM/chatbot techniques
• ✓ Deploy models to production with APIs, Docker and MLOps basics

Prerequisites

Basic Python is recommended (see our Python course first). You can start with everyday maths — algebra and a little statistics help for deeper work, but the libraries handle most of the heavy lifting so you learn by building.

👋 New to AI? The best time to start is now.

You'll need basic Python — if you have that, you're ready. Start with Lesson 1 and build your first ML model.

Continue where you left off

Lessons in this course

• Introduction to AI & ML — What AI and machine learning are, and what you'll be able to build
• Python for Machine Learning — NumPy, Pandas, and Matplotlib — the essential Python tools for ML
• Data Preprocessing — Clean, transform, and prepare raw data before training any model
• Linear Regression — Predict continuous values — your first machine learning model
• Classification Basics — Categorise data into classes using logistic regression and k-NN
• Decision Trees & Random Forests — Build interpretable tree models and ensemble them into random forests
• Neural Networks Introduction — Understand neurons, layers, weights, and how neural networks learn
• Deep Learning Fundamentals — Train deep neural networks with backpropagation and activation functions
• Natural Language Processing — Process and understand text — tokenisation, embeddings, and sentiment analysis
• Computer Vision Basics — Teach computers to understand images with CNNs and image classification
• Advanced Neural Networks — Regularisation, batch normalisation, dropout, and advanced architectures
• Transformers & LLMs — How attention mechanisms power GPT, BERT, and large language models
• Reinforcement Learning — Train agents to make decisions with rewards, policies, and Q-learning
• Model Deployment — Serve ML models in production with FastAPI, Docker, and cloud platforms
• Support Vector Machines — Maximum-margin classifiers, support vectors and the kernel trick
• K-Means & Clustering — Unsupervised grouping with k-means, choosing k, DBSCAN and hierarchical
• Dimensionality Reduction with PCA — Principal components, explained variance and when to reduce dimensions
• Unsupervised Learning — Finding structure in unlabelled data — clustering, association and density estimation
• Dimensionality Reduction — Beyond PCA — t-SNE, UMAP and choosing how many dimensions to keep
• Ensemble Methods — Bagging, boosting and stacking — why combining weak models beats one strong one
• Feature Engineering & Selection — Scaling, encoding, interactions, selection and avoiding data leakage
• Model Selection & Tuning — Cross-validation, grid and random search, and avoiding tuning on your test set
• Time Series Forecasting — Working with ordered data — lags, windows, and why random splits break it
• Gradient Boosting (XGBoost & LightGBM) — Sequential weak learners, XGBoost/LightGBM and key hyperparameters
• Time-Series Forecasting (ARIMA & Prophet) — Trend, seasonality, stationarity, ARIMA/SARIMA and Prophet
• Anomaly Detection — Isolation Forest, One-Class SVM, LOF and statistical methods
• Model Interpretability (SHAP & LIME) — Explain black-box models with feature importance, SHAP and LIME
• LLM Agents & Tool Use — Function/tool calling, the ReAct loop, planning and guardrails for agentic LLMs
• Handling Imbalanced Data — Precision/recall over accuracy, SMOTE resampling, class weights and threshold tuning
• Residual Networks (ResNet), DenseNets & Modern CNN Design — Understand skip connections, dense blocks, and modern CNN innovations
• Training Stability Techniques: Normalization, Initialization, Gradient Clipping — Batch norm, layer norm, Xavier/He init, and gradient clipping for stable training
• Generative Models: Autoencoders, VAEs & GANs — Build models that generate new data — autoencoders, VAEs, and GANs
• Diffusion Models Explained (Stable Diffusion, DDPM) — How denoising diffusion models generate images from noise
• Large Language Models Architecture (GPT, LLaMA, Mistral) — Decoder-only transformers, tokenisation, and the architecture of modern LLMs
• Tokenization Strategies (BPE, WordPiece, SentencePiece) — How BPE, WordPiece, and SentencePiece convert text to tokens for LLMs
• Fine-Tuning LLMs: LoRA, QLoRA & PEFT Techniques — Fine-tune LLMs efficiently on custom data with LoRA and QLoRA
• Reinforcement Learning Basics (MDP, Policies, Rewards) — Markov Decision Processes, value functions, policies, and the Bellman equation
• Q-Learning & Deep Q-Networks (DQN) — Implement Q-learning and DQN with experience replay and target networks
• Policy Gradient Methods (REINFORCE, PPO, A2C) — Train agents directly on policy gradients with PPO and actor-critic methods
• Computer Vision Pipelines with OpenCV & PyTorch/TensorFlow — Build end-to-end vision pipelines for classification, detection, and segmentation
• Object Detection: YOLO, SSD & Faster R-CNN Models — Detect and localise objects in images with YOLO and two-stage detectors
• Semantic Segmentation (U-Net, DeepLab, Mask R-CNN) — Label every pixel in an image with U-Net and DeepLab architectures
• Speech Recognition & Audio ML Models — Build speech-to-text systems with Whisper, mel spectrograms, and CTC
• Advanced NLP: Transformers, BERT, T5, LLaMA, Mistral — Fine-tune BERT for classification, T5 for generation, and LLaMA for chat
• Building Retrieval-Augmented Generation (RAG) Systems — Combine LLMs with vector search to build knowledge-grounded chatbots
• Prompting AI Coding Assistants (Claude & ChatGPT) — Get better code from AI: precise prompts, exact error reports, and targeted follow-ups
• Vector Databases & Embeddings (FAISS, Pinecone, ChromaDB) — Store and search embeddings at scale with FAISS, Pinecone, and Chroma
• Evaluating AI Models: F1, ROC, Perplexity, BLEU, WER — Choose and calculate the right metrics for classification, NLP, and generation tasks
• Model Compression: Quantization, Pruning, Distillation — Make models smaller and faster with int8 quantisation, pruning, and distillation
• Optimizing Models for CPU/GPU/TPU Deployment — Optimise inference for different hardware targets with ONNX, TensorRT, and XLA
• Distributed Training with Data Parallelism & Model Parallelism — Train large models across multiple GPUs with DDP, FSDP, and pipeline parallelism
• Serving ML Models: TorchServe, FastAPI, TensorFlow Serving — Deploy and serve ML models reliably with TorchServe, FastAPI, and TF Serving
• Monitoring Models in Production (Drift, Outliers, Bias) — Detect data drift, outliers, and model degradation in production systems
• MLOps Fundamentals: Pipelines, CI/CD, Versioning — Automate ML pipelines with MLflow, DVC, and CI/CD for model releases
• Building Recommender Systems (Content, Collaborative, Hybrid) — Build content-based, collaborative filtering, and hybrid recommendation engines
• Graph Neural Networks (GNNs) for Social & Knowledge Graphs — Apply GNNs to social networks, knowledge graphs, and molecular data
• AutoML & Neural Architecture Search (NAS) — Automate model selection and architecture design with AutoML and NAS
• Ethical AI, Bias Mitigation & Safety Principles in ML — Identify, measure, and reduce bias — build fair and responsible AI systems
• Final AI Project — Build & Deploy a Full End-to-End ML System — Design, train, evaluate, and deploy a complete ML system from scratch

Frequently asked questions

Do I need to be good at maths to learn AI?

You can start building AI projects with basic maths and grow your understanding over time. A working grasp of algebra and statistics helps for deeper machine-learning work, but modern libraries handle most of the heavy lifting so you can learn by doing.

What programming language is best for AI and machine learning?

Python is by far the most popular language for AI, thanks to libraries like TensorFlow, PyTorch, scikit-learn and pandas. This course uses Python for its examples.

How do I start learning AI as a beginner?

Start with the core concepts — what machine learning is, how models learn from data, and the basics of neural networks — then build small projects like a classifier or chatbot. This course introduces each idea with approachable, hands-on examples.

Is a career in AI worth it in 2026?

AI and machine learning roles are among the fastest-growing and best-paid in tech, and AI skills increasingly add value in almost every field, making it one of the most future-proof areas to learn.

More for this course

• AI & Machine Learning cheat sheet — Quick syntax reference you can scan while you code
• Getting Started with Machine Learning in Python — An introduction to machine learning concepts and how to implement them using Python libraries.
• Neural Networks: An Introduction — Understand the basics of neural networks and how they power modern AI systems.
• Building AI Chatbots with Natural Language Processing — Learn to build intelligent chatbots using NLP, from simple rule-based systems to advanced transformer models. Master intent classification, entity extraction, and deployment.
• Computer Vision with OpenCV and Python — Master image processing, face detection, object recognition, and real-time video analysis. Learn edge detection, color spaces, contours, and deep learning integration.