DeepLearning.AI courses

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.

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.

Awesome LLM Apps is an open-source GitHub collection of Python templates for AI agents, agent skills, and retrieval-augmented generation (RAG) applications. Its examples include starter and advanced agents, multi-agent teams, voice and multimodal agents, MCP-based agents, memory-enabled agents, data analysis, web scraping, and other application patterns. The repository provides runnable source projects and step-by-step tutorials. Individual agents can be cloned and run with an API key, while agent skills can be installed with a command such as `npx skills add` and used with coding agents including Claude Code, Codex, and Cursor. The projects support models from providers and ecosystems including Claude, Gemini, GPT, DeepSeek, Llama, and Qwen. The repository describes the collection as free and open source under the Apache-2.0 license.

DeepTutor is an open-source, self-hostable AI tutoring platform developed by HKUDS for lifelong personalized tutoring. It provides a web application and command-line interface for interactive learning, problem solving, quiz generation, deep research, visualization, and mastery practice, with persistent memory and learning state shared across knowledge bases, books, notebooks, tutor personas, and other activities. The platform supports multi-agent problem solving, retrieval-augmented learning with source-traced and page-level citations, math animations, interactive visualizations, and tutor bots built from personal study materials. Its knowledge sources include personal documents, EPUBs and books with annotations, GitHub repositories, web search, and connected libraries; documented retrieval and ingestion options include GraphRAG, PageIndex, LightRAG, linked knowledge bases, Obsidian, and configurable parsing and vector backends. DeepTutor also includes courses, research workflows, an ecosystem of MCP services and tool or capability plugins, and integrations with connected coding agents and other partners. It can run locally or through Docker.

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.