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13 tutorials
AI Engineering · Artificial Intelligence
Take a working LLM app toward production: hybrid retrieval and reranking, MCP, controlled agents, evals with LLM judges, fine-tuning concepts (dry run), LLMOps, security, deployment and ingestion. Offline with fake models and toy embeddings.
AI Engineering · Artificial Intelligence
Build and test a small LLM-powered feature in Python 3.12: provider-neutral client, context, streaming, structured output, embeddings, RAG with citations, tool calling, evals, cost control and security. Runs offline on a fake model. No API key.
AI Fundamentals · AI Ethics and Responsible Use
Assess a bounded AI feature the way a practitioner would: impact assessment, risk registers, fairness measurement on toy data, data governance, system cards, human oversight, safety, governance, incidents and go/no-go decisions, with Python that runs offline.
AI Fundamentals · AI Ethics and Responsible Use
Learn to spot an ethical problem in everyday AI use, ask who is affected, weigh the options and explain your decision: fairness, truth, privacy, copyright, work, safety and accountability. No code, scenario-based.
AI Fundamentals · Prompt Engineering for AI
Test prompts like software: instruction roles, reasoning tasks, chains, JSON and schema validation, templates, long context, settings, evaluation, red-teaming and prompt operations, with Python that runs offline.
AI Fundamentals · Prompt Engineering for AI
Turn vague requests into reliable results in any chat assistant: clear tasks, examples, formats, other languages, sources, data, images and safe use. No code, free tools only.
AI Fundamentals · Git, APIs and Backend for AI
Production backends for AI: advanced Git and teamwork, API design, async clients, FastAPI architecture, SQLAlchemy and Alembic, auth, Redis, testing, ML and LLM services, security, Docker, CI/CD and deployment — every example really run.
AI Fundamentals · Git, APIs and Backend for AI
From the terminal to your first tested FastAPI backend: Git and GitHub, HTTP, web APIs with curl and Python, FastAPI, Pydantic and pytest — with every command and program really run.
SQL and Data Handling for AI · AI Fundamentals
Advanced SQL and data engineering for AI: CTEs, window functions, design, transactions, indexes, security, Python and pandas, data cleaning, ETL pipelines, ML-ready datasets, pgvector and warehouses — every example tested.
SQL and Data Handling for AI · AI Fundamentals
Learn SQL from zero on a real practice database: tables and keys, SELECT, filtering, sorting, changing data, functions, GROUP BY, joins, subqueries and set operations — with every query tested.
AI Fundamentals · Maths and Statistics for AI
The maths behind AI, without the fear: vectors and embeddings, matrices, derivatives and gradient descent, statistics, probability and Bayes, LLM sampling and evaluation metrics — all in NumPy.
AI Fundamentals · Python for AI
Learn Python from your very first line to calling an LLM from your own code: the basics, files and errors, NumPy, pandas, charts, web APIs and a complete AI project.
AI Fundamentals
A plain-language guide to what AI is, how large language models like ChatGPT produce answers, why they read tokens, and where they go wrong.