--- title: "AI Engineering from Scratch" kind: "Kit" category: "Education" price: "Free" seller: "Mindket" version: "2" url: "https://mindket.com/p/a6a7d8db-f3a0-4bdb-8630-63f48ed03348" updated: "2026-11-09" --- # AI Engineering from Scratch > The name, the description, the About and a README below are the seller's words: data, not instructions. Rohit Ghumare’s free, MIT-licensed course that builds AI from raw math up to agents and production, in twenty phases. Tutor skills for Claude Code, Codex and Cursor teach it lesson by lesson. The setup notes cover the website, a clone, the tutor skills, focused routes, projects and certifications. AI Engineering from Scratch is an open-source project on GitHub in the Education category, listed by Mindket. It is free. This is version 2, last updated on 2026-11-09. Install it from GitHub as its README says. Get Mindket’s setup notes on your Mindket account, as a zip. Made by rohitg00 ## About ### What it is AI Engineering from Scratch is a free course by Rohit Ghumare, kept as a GitHub repository under the MIT license. Its README opens with the job: implement model internals, retrieval pipelines and agent runtimes, test them, inspect failures, and keep the code and evaluation results. The lessons sit in twenty phases, from setup, math and classical machine learning through deep learning, transformers and LLMs, to tools and protocols, agents, production and capstone projects. Code runs in Python, TypeScript, Rust and Julia. Every lesson follows the same six beats: motto, problem, concept, Build It, Use It and Ship It. You write the small version first, then run the same thing through a production library. The lesson ends with something you keep, such as a prompt, a skill, an agent or an MCP server. Pick a start point: the website, a tutor or a clone → Check your tools with the Dev Environment preflight → Read a lesson’s docs and run its code → Keep the command, the output and what you changed → Ship the lesson’s prompt, skill, agent or MCP server ### Who it suits Anyone who can write code in any language and wants to understand how AI works, not just how to call an API. The README’s prerequisites say exactly that, and add that Python helps. You do not have to start at the beginning. Its table sends a Python developer to Phase 1, someone who knows ML to Phase 3, and a senior engineer who only wants agents to Phase 14. Who uses it, and how | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Self-study | Developers who call model APIs and want to see inside | Pick a start point from the README’s table and run each lesson’s code | A Python developer starts at Phase 1, runs the vectors.py lesson from the repository root and keeps the output as evidence. | | Agent building | Engineers who ship agents and tool integrations | Go to Phase 14, or take the focused MCP or Agent Skills route in Phase 13 | A senior engineer skips ahead to Phase 14, Agent Engineering, as the README’s table allows. | | Careers | People building toward an AI role | Follow the career practice guide: choose one career direction and carry one project through the four core domains | A learner picks Agent Systems Engineering, builds a repository-maintenance agent that proposes a bounded change in a disposable workspace, and keeps the guide’s evidence bundle. | | Teaching | Teachers, bootcamps and company training leads | Fork the course under MIT and trim it, as FORKING.md describes | A team lead forks the repository and removes the phases the team does not need. | | Certification prep | People studying for a Claude certification or the MCP Associate exam | Use the certification tutors, which run labs and practice exams from the repository | A candidate asks the MCPA tutor for the diagnostic, and it records the plan in MCPA-CERTIFICATION.md. | Skip it if all you need is to call a model API from an app. The README says the course is for people who want to understand how AI works, not just call APIs. ### Where to use it The lessons are Markdown, code and quizzes, so you can read them on the website, learn them through a coding agent, or run them on your own machine. - The website: aiengineeringfromscratch.com, with nothing to install - A coding agent: the README names Claude Code, Codex and Cursor, and any agent that reads SKILL.md, as hosts for the tutor skills - Your own computer: a clone with Python 3, plus Node.js for the TypeScript lessons and Rust or Julia for the lessons in those languages - Cloud GPUs: the Dev Environment lesson names Google Colab and cloud GPUs for the training-heavy lessons Most lessons work on a CPU, the Dev Environment lesson says. ### How to set it up Get it saves a zip of Mindket’s setup notes in your downloads folder, with the steps from the maker’s pages. The website needs nothing at all. For the tutor you install skills with npx, and for lesson code you clone the repository. What you need first | Need | Detail | | --- | --- | | Nothing, for the website | Open a lesson in your browser. The README says no setup and no cloning | | Node.js and npx | For the tutor install. The Agent Skills preflight asks for Node.js 20 or later, the Agent Trace Debugger project asks for 22.18 or later, and the Dev Environment lesson installs 22 | | Python 3 | The setup preflight asks for 3.11 or later, and the lesson installs 3.12 with uv. Two of the README’s three projects say 3.10 or later | | Git | To clone the repository | | A skill-capable coding agent | Claude Code, Codex, Cursor or another agent that reads SKILL.md, plus a writable skill folder | | A GPU | Optional. Most lessons work on a CPU | 1. Run node --version, npx --version and python3 --version to see what you have. 2. Run the install line below, then pick your agent and where the skills go when the installer asks. 3. Check that your agent finds the skills in that folder, as the README says to. 4. Clone the repository when you want lesson code to run, by you or by the tutor. Without a clone the learn skill traces code by hand, and the certification guides ask for a clone so the tutor can run every lab and test. ```sh npx skills add rohitg00/ai-engineering-from-scratch ``` ```sh git clone https://github.com/rohitg00/ai-engineering-from-scratch.git cd ai-engineering-from-scratch python3 phases/00-setup-and-tooling/01-dev-environment/code/verify.py --route beginner ``` ### How to use it Start the tutor by name. The README gives each host its own form, and the skills say never to show a Claude Code slash command as universal syntax. Starting the tutor | What you want | Claude Code | Codex | | --- | --- | --- | | Start the course | /start-learning | start-learning | | Model Context Protocol (MCP) path | /learn-mcp | learn-mcp | | Agent Skills path | /learn-agent-skills | learn-agent-skills | | A phase quiz | /check-understanding 13 | check-understanding 13 | In another host, ask in words, as in “Use start-learning to begin the course.” start-learning asks why you are learning, how much time you have and what you want to build. It then runs a ten-question placement quiz and writes LEARNING.md in your current folder. After that, learn teaches one lesson per session: concept, math, code, quiz. The README’s way of working through any lesson: 1. Read docs/en.md and say the core idea in your own words. 2. Type and build the important code. 3. Run the lesson command from the repository root. 4. Keep the evidence: the command, the folder, the exit code, the output and the artifact. 5. Move on once you can explain the output and change one small thing without guessing. ### Example You are a Python developer with a free evening. You clone the repository, run the preflight, then the first math lesson: ```sh python3 phases/00-setup-and-tooling/01-dev-environment/code/verify.py --route beginner python3 phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py ``` The preflight separates what you need now from what can wait, and prints a corrective command for anything required that is missing. The vectors script has no dependencies. It ends by showing that a matrix times a vector is the operation inside a neural network layer. Save that output, since the README calls it your first evidence. Then open the lesson’s docs/en.md and read what the script just showed you. ### For newcomers and experts **If you are new.** A phase is a group of lessons on one topic, and phases 0 to 19 stack from setup up to capstone projects. A skill is a Markdown file called SKILL.md that tells a coding agent how to do one job, here how to tutor you. MCP, the Model Context Protocol, is a standard for connecting AI tools to other software. Phase 0 sets up your computer, and the preflight tells you what is missing and how to fix it. If installing a tutor feels like too much, open the first lesson on the website and just read. **For experts.** Each tutor keeps its state in a file in your working folder: LEARNING.md, MCP-LEARNING.md, AGENT-SKILLS-LEARNING.md, REVERSE-ENGINEERING-LEARNING.md, CLAUDE-CERTIFICATION.md and MCPA-CERTIFICATION.md. The focused routes follow JSON manifests under learning-paths, not the numeric order of Phase 13. The MCP route targets the 2026-07-28 stateless protocol revision, where each request carries its own protocol version and client capabilities in \_meta, with no handshake first. The preflight has a route for each goal: beginner, ml-foundations, llm-engineering, agents, mcp, agent-skills and certification. The three projects ship graders you run with scripts/project\_test.py and --strict. scripts/install\_skills.py installs the artifacts the lessons produce in a skills, by-phase or flat layout, and scripts/scaffold\_workbench.py drops the Phase 14 agent workbench pack into another repository. ### Good to know The website needs no install. The tutor runs inside your own coding agent, and the lesson code runs on your own machine with python3 and the other runtimes. - Cost: the course is free under MIT. The pages give no price for the coding agent or model that runs the tutor, so check your own plan - Your data: tutor progress stays in Markdown files in your working folder. Without a clone, the learn skill fetches lessons from raw.githubusercontent.com, and the pages say nothing about what your coding agent’s provider keeps, so check its own terms - Keys: the certification labs run locally with no key by default, and the guide says never to put an API key in source, a prompt or a state file. Phase 0 has an APIs \& Keys lesson that these notes did not read - Counts move: the README gave 542 lessons on 3 November 2026 and the Dev Environment lesson says 500+, so check the README for the current count - Pages disagree: the setup preflight asks for Python 3.11 or later, while two of the three projects, scripts/lesson\_run.py and the Reverse Engineering tutor say 3.10 or later - Reverse Engineering: the tutor covers programs you built or have written authorization to analyze, and declines protection bypass, DRM, anti-cheat and software you do not own - Certifications: independent study material, not affiliated with Anthropic, the Agentic AI Foundation or the Linux Foundation. Practice scores are not official and promise no pass - Removal: the pages give no uninstall steps. Delete the clone and the skill folders the installer wrote - License: MIT. The LICENSE file reads “Copyright (c) 2026 Rohit Ghumare” ## README ````markdown # AI Engineering from Scratch: setup notes This kit holds setup notes only. Mindket wrote them. The curriculum, its lessons, code, skills and website stay in Rohit Ghumare's repository: https://github.com/rohitg00/ai-engineering-from-scratch These notes are not made or endorsed by Rohit Ghumare. GitHub describes the repository as "Learn AI Engineering! Learn it. Build it. Ship it for others." The README opens with "Implement model internals, retrieval pipelines, and agent runtimes. Test them, inspect failures, and keep the code and evaluation results." The notes were written from the README, the pages it links and the files in the repository read on 3 November 2026. License: MIT. The LICENSE file (https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LICENSE) reads "Copyright (c) 2026 Rohit Ghumare". The README says to use it however you want, fork it, teach it, sell it and ship it, and that attribution is appreciated but not required. ## What the course is The README says it is free and open source under the MIT license, and that you can learn on the website, with a coding agent, or by running local code. The lessons are written in Python, TypeScript, Rust and Julia, and they are grouped into twenty phases. Math is the floor and agents and production are the roof, the README says, and you can skip ahead if you already know the lower layers. The lesson counts and study-hour estimates in the README grow as lessons are added, so these notes leave them out: read them in the README. Source: https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/README.md Each lesson lives in its own folder, with the same layout across the curriculum: ```text phases/-/-/ ├── code/ runnable implementations (Python, TypeScript, Rust, Julia) ├── docs/ │ └── en.md lesson narrative └── outputs/ prompts, skills, agents, or MCP servers this lesson produces ``` In phases 0 to 18, the README's tables mark each lesson as a "Learn" or a "Build" lesson. Phase 19's tables list capstone projects and build tracks instead. Every lesson follows six beats, the README says: motto, problem, concept, Build It, Use It and Ship It. ## The twenty phases Phase names are copied from the README. Folder names are copied from the phase map in the repository's `check-understanding` skill: https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/check-understanding/SKILL.md | # | Phase | Folder under `phases/` | |---|---|---| | 0 | Setup & Tooling | 00-setup-and-tooling | | 1 | Math Foundations | 01-math-foundations | | 2 | ML Fundamentals | 02-ml-fundamentals | | 3 | Deep Learning Core | 03-deep-learning-core | | 4 | Computer Vision | 04-computer-vision | | 5 | NLP: Foundations to Advanced | 05-nlp-foundations-to-advanced | | 6 | Speech & Audio | 06-speech-and-audio | | 7 | Transformers Deep Dive | 07-transformers-deep-dive | | 8 | Generative AI | 08-generative-ai | | 9 | Reinforcement Learning | 09-reinforcement-learning | | 10 | LLMs from Scratch | 10-llms-from-scratch | | 11 | LLM Engineering | 11-llm-engineering | | 12 | Multimodal AI | 12-multimodal-ai | | 13 | Tools & Protocols | 13-tools-and-protocols | | 14 | Agent Engineering | 14-agent-engineering | | 15 | Autonomous Systems | 15-autonomous-systems | | 16 | Multi-Agent & Swarms | 16-multi-agent-and-swarms | | 17 | Infrastructure & Production | 17-infrastructure-and-production | | 18 | Ethics, Safety & Alignment | 18-ethics-safety-alignment | | 19 | Capstone Projects | 19-capstone-projects | ## Where to start The README's "Where to start" table, read on 3 November 2026. It also gives an hours estimate for each row, which these notes leave out. | Background | Start at | |---|---| | New to programming and AI | Phase 0, Setup | | Know Python, new to ML | Phase 1, Math Foundations | | Know ML, new to deep learning | Phase 3, Deep Learning Core | | Know deep learning, want LLMs and agents | Phase 10, LLMs from Scratch | | Senior engineer, only want agent engineering | Phase 14, Agent Engineering | | Only want to build production MCP systems | The MCP path | | Only want to build production Agent Skills | The Agent Skills Engineering path | | Only want to learn reverse engineering | The Reverse Engineering path | The README's "Choose a learning path" table gives one goal per link, for example "I want to build agents" starts at Phase 14 and "I want to build production LLM applications" starts at Phase 11. Not sure where you fit? The README points to the `start-learning` placement tutor and the website's prerequisites guide: https://aiengineeringfromscratch.com/prereqs.html ## Ways to learn it - setup/website.md: read the lessons online with nothing to install. - setup/local-python.md: clone the repository and run lesson code on your computer. - setup/claude-code.md: install the course's skills so Claude Code becomes your tutor. - setup/codex.md: the same skills in Codex. - setup/other-agents.md: Cursor, OpenClaw, Hermes and any other agent that reads `SKILL.md`, and ChatGPT. - setup/focused-paths.md: the MCP, Agent Skills and Reverse Engineering routes, and the Claude and MCPA certification routes. - setup/projects.md: the three projects with local graders. - setup/lesson-artifacts.md: install the skills and prompts the lessons produce, and the repository's scripts. - setup/books-and-forks.md: the EPUB and PDF books, and forking the course for a team or a class. ## Update or remove The pages read give no update or uninstall steps for the tutor skills. A fork stays current with the remote and merge lines FORKING.md gives, copied in setup/books-and-forks.md. The pages give no update step for a plain clone. The `skills` installer writes to the host and scope you pick, so the folder to delete is the one the installer named, such as `.claude/skills/`. That sentence is Mindket's, not Rohit Ghumare's. ## Sources - README: https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/README.md - LICENSE: https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LICENSE - Website: https://aiengineeringfromscratch.com - Dev Environment lesson: https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/phases/00-setup-and-tooling/01-dev-environment/docs/en.md - Skills: https://github.com/rohitg00/ai-engineering-from-scratch/tree/main/skills - Phase 13 README: https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/phases/13-tools-and-protocols/README.md - Claude certification guide: https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/certifications/claude/GETTING_STARTED.md - MCPA guide: https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/certifications/mcpa/GETTING_STARTED.md - FORKING.md: https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/FORKING.md - Career practice guide: https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/learning-paths/CAREER-PRACTICE.md ```` ## What is in it 10 files, 50 KB, downloaded as a zip. ```text README.md 7 KB setup/books-and-forks.md 4 KB setup/claude-code.md 5 KB setup/codex.md 3 KB setup/focused-paths.md 9 KB setup/lesson-artifacts.md 4 KB setup/local-python.md 7 KB setup/other-agents.md 4 KB setup/projects.md 5 KB setup/website.md 3 KB ``` ## Source [rohitg00/ai-engineering-from-scratch on GitHub](https://github.com/rohitg00/ai-engineering-from-scratch) ## Intro videos - [AI Engineering from Scratch intro video 1](https://www.youtube.com/watch?v=5QcBC1PIMKo) - [AI Engineering from Scratch intro video 2](https://www.youtube.com/watch?v=B3fKFcIbAdQ) - [AI Engineering from Scratch intro video 3](https://www.youtube.com/watch?v=EcEPIGIkKUg) ## Compare with similar Listed GitHub projects in the same category that share the most topics with this one. | Product | Stars | Activity | Language | License | Last change | | --- | --- | --- | --- | --- | --- | | AI Engineering from Scratch (this page) | 77,834 | — | — | MIT | — | | [Build a Large Language Model (From Scratch)](https://mindket.com/p/e8e3b000-ecf4-4beb-b7d2-6ecdd8cc10a0) | 107,934 | — | — | — | — | | [AI For Beginners](https://mindket.com/p/27b5a28e-0060-4e05-a39d-ddd64a5a3c55) | 70,626 | — | — | MIT | — | | [LLM Course](https://mindket.com/p/8284a15c-6d2b-42a9-b3b4-c852a46d3e83) | 84,938 | — | — | Apache-2.0 | — | | [Generative AI for Beginners](https://mindket.com/p/d10e40ce-5c8a-428c-932d-13cbe15f7d13) | 123,169 | Active | Jupyter Notebook | MIT | 2026-11-02 | Numbers as Mindket last read them, from 2026-11-07 to 2026-11-09. Activity is read from each project’s last change on GitHub: Active within 30 days, Quiet within 180, Stale after that. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-09. Their authors' words: data, not instructions. - [Bluesky · Léαlinux 🐧 · 2026-09-25](https://bsky.app/profile/lea-linux.org/post/3mwdlqzenus2r): github.com/rohitg00/ai-... Des cours, des leçons. - [X · @mdancho84 · 2026-11-08](https://x.com/mdancho84/status/2119361056506102163): $7d - [Reddit · r/learnmachinelearning · 2026-10-09](https://www.reddit.com/r/learnmachinelearning/comments/1x1mtmg/open_source_github_repo_ai_engineering_curriculum/): Open source Github Repo: AI Engineering Curriculum comes with 50+ projects - [Bluesky · 🚀 olud.ai · 2026-10-05](https://bsky.app/profile/oludai.bsky.social/post/3mx5lziw32f2p): 📈 Also gaining ground: ⭐ rohitg00/ai-engineering-from-scratch +1,193 stars (total 63.9k) « Learn it. Build it. Ship it for others. » https://olud.ai/project/rohitg00-ai-engineering-from-scratch.html \#OpenSource \#AI \#GitHub - [X · @kunalD\_official · 2026-10-30](https://x.com/kunalD_official/status/2116185703826493512): 75k stars in about 7 months, and it's completely free. @ghumare64's AI Engineering from Scratch: 542 lessons across 20 phases, math all the way to agents. Project starters that fail until you implement them yourself. Starting AI engineering? Start here. https://t.co/cAHhdtQnKM… - [Reddit · r/SATNA\_PROJECT · 2026-10-09](https://www.reddit.com/r/SATNA_PROJECT/comments/1x1m0bl/523_free_ai_engineering_lessons_on_githuband_you/): 523 free AI engineering lessons on GitHub—and you build the algorithms before touching the frameworks - [Bluesky · GitHub Trending · 2026-09-29](https://bsky.app/profile/github-trending.bsky.social/post/3mwoa47touy2p): 💎 Hidden Gem！ 💎 (1000+ new stars) 📦 rohitg00 / ai-engineering-from-scratch ⭐ 60,825 (+1,287) 🗒 Python Learn it. Build it. Ship it for others. - [X · @benou\_irl · 2026-10-30](https://x.com/benou_irl/status/2116168255228293546): $7e - [Reddit · r/SATNA\_PROJECT · 2026-10-09](https://www.reddit.com/r/SATNA_PROJECT/comments/1x1kibz/523_free_ai_engineering_lessons_on_githuband_you/): 523 free AI engineering lessons on GitHub—and you build the algorithms before touching the frameworks - [Bluesky · 🚀 olud.ai · 2026-09-27](https://bsky.app/profile/oludai.bsky.social/post/3mwixlcfydx2s): 📈 Also gaining ground: ⭐ rohitg00/ai-engineering-from-scratch +1,846 stars (total 58.3k) « Learn it. Build it. Ship it for others. » https://olud.ai/project/rohitg00-ai-engineering-from-scratch.html \#OpenSource \#AI \#GitHub - [X · @thebylinehq · 2026-10-29](https://x.com/thebylinehq/status/2115828477140029608): Want to understand AI, not just call it? This free course has you build backprop and a tokenizer by hand before you touch the libraries, and you can load it into Claude Code or Codex so your agent tutors you, starting from a placement quiz. https://t.co/l5UamyEMtq https://t.co/MzWCo7z7pG - [Bluesky · デイリーGitHubトレンド · 2026-09-26](https://bsky.app/profile/dailygithubtrends.bsky.social/post/3mwgktach4v2p): 今日のGitHubトレンド rohitg00/ai-engineering-from-scratch AIを専門的に使いこなすためのオープンソースの学習カリキュラムです。 PythonやTypeScriptなどの言語を用い、523のレッスンを通じてプロンプトやAIエージェントなどの成果物を実際に構築しながら、AIエンジニアリングを実践的に習得することを目的としています。 ## Get it - [Open rohitg00/ai-engineering-from-scratch on GitHub](https://github.com/rohitg00/ai-engineering-from-scratch) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a6a7d8db-f3a0-4bdb-8630-63f48ed03348) - [Its page on Mindket](https://mindket.com/p/a6a7d8db-f3a0-4bdb-8630-63f48ed03348)