AI & Machine Learning Tutorial

Master artificial intelligence and ML — 50 lessons from your first model to advanced LLMs, computer vision, and production deployment.

Learn AI & machine learning from scratch — Python ML tools, regression, classification, neural networks, deep learning, NLP, computer vision, transformers…

Part of the free AI & Machine Learning course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

What you'll learn

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.

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

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
• 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
• 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
• Gradient Boosting (XGBoost & LightGBM) — Sequential weak learners, XGBoost/LightGBM and key hyperparameters
• Feature Engineering & Selection — Scaling, encoding, interactions, selection and avoiding data leakage
• 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

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• Cheat sheets & quizzes
• Coding dictionary