Machine Learning Crash Course

AI Engineering Lab is a free, self-paced 24-week AI engineering course developed by Zorost Intelligence AI Lab. It takes learners from Python and machine learning through deep learning, LLM internals, prompt and context engineering, retrieval-augmented generation with vector search, quantization, LoRA and DPO fine-tuning, evaluation harnesses, coding-agent harnesses, AI agents, MCP, and cloud and Databricks platforms including Azure AI Foundry, Google Vertex AI, AWS Bedrock, and Databricks. The curriculum uses one continuous fictional freight-company case study. Each week pairs a concept with a runnable Python, SQL, or PySpark artifact; from Week 3 onward, the work includes a metric and an error note. The repository contains 43 runnable notebooks, publishes the full curriculum online, requires no signup or paid API key, and states that the first eight weeks require no GPU. It is MIT licensed.

The Hugging Face LLM Course is a free educational course covering large language models, natural language processing, and the Hugging Face ecosystem. It teaches Transformer concepts and use of Transformers, Datasets, Tokenizers, Accelerate, and the Hugging Face Hub; later chapters cover classic NLP tasks, model demos, fine-tuning, dataset curation, and reasoning models. The course includes code that can be run in Google Colab or Amazon SageMaker Studio Lab, requires good Python knowledge, and recommends prior introductory deep-learning study. It is released under the Apache 2 license, with contributions supported through issues, translations, and the course repository.

DeepLearning.AI courses is an online catalog of AI education from DeepLearning.AI, covering prompting, retrieval-augmented generation, agents, generative models, LLM operations, search and retrieval, AI coding, and related subjects. The catalog is organized into short courses, broader courses, and professional certificates with beginner and intermediate levels, topic filters, and collaborator listings. Featured courses include AI Prompting for Everyone, Build with Andrew, and Agentic AI; the video notes that many courses are free and can be audited.

AI Engineering from Scratch is a free, open-source curriculum developed by rohitg00 for learning to build and ship AI systems end to end. Its 20-phase sequence covers development tooling, mathematical and machine-learning foundations, deep learning, NLP, computer vision, generative AI, LLM applications, agent engineering, Model Context Protocol (MCP), agent skills, reinforcement learning, and related topics, with implementations in Python, TypeScript, Rust, and Julia. The repository describes 511 lessons and approximately 329 hours of material; each lesson produces a reusable artifact such as a prompt, skill, agent, or MCP server. Learners are instructed to read the lesson documentation, type and run the code from the repository root, record command evidence and outputs, and make a small change before continuing. It provides routes for complete foundations, mathematics and machine learning, production LLM applications, agent engineering, MCP, agent skills, and Claude certification preparation. The repository is distributed under the MIT license and is accompanied by a website containing the same lesson content.

Rebuild YouTube with AI is a live, hands-on cohort course from ByteByteGo about building and deploying a YouTube-like video site with AI coding tools. It covers decomposing the product into core surfaces, generating interface mockups, implementing React and Next.js pages, modeling data in Postgres, adding authentication and uploads, generating media, storing video with Cloudflare, and deploying with Vercel. The course also implements search and related videos with multimodal embeddings and pgvector, then discusses recommender-system limitations, watch-time tracking, AI-driven Playwright testing, and production workflows. Sessions are recorded, and enrollment includes course materials and access to the cohort recordings after the live course.