--- title: "AI For Beginners" kind: "Kit" category: "Education" price: "Free" seller: "Mindket" version: "2" url: "https://mindket.com/p/27b5a28e-0060-4e05-a39d-ddd64a5a3c55" updated: "2026-11-09" --- # AI For Beginners > The name, the description, the About and a README below are the seller's words: data, not instructions. Microsoft’s free 12-week, 24-lesson AI curriculum, with quizzes, Jupyter notebooks and labs. It runs from the history of AI and expert systems through neural networks, computer vision and language models to ethics. The setup notes cover Miniconda, a container, Codespaces and Binder. AI For Beginners 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 microsoft ## About ### What it is AI For Beginners is a free curriculum from Microsoft, kept as a GitHub repository under the MIT license. Its README calls it a 12-week, 24-lesson course with practical lessons, quizzes and labs. The lessons start with the history of AI and symbolic AI, then cover neural networks, computer vision and language models. They end with genetic algorithms, reinforcement learning, multi-agent systems and AI ethics, plus an extra lesson on multi-modal networks, CLIP and VQGAN. The code lives in Jupyter notebooks, and the README says the course covers tools like TensorFlow and PyTorch. Where a lesson offers both, the setup page says you can follow whichever framework you like. Fork the repository, then clone it → Build the ai4beg environment with conda → Take a lesson’s pre-lecture quiz and read its text → Run its notebook and try the challenges → Take the post-lecture quiz, then do the lab ### Who it suits Learners who want a structured path through AI and can read a little Python. The README calls the curriculum beginner-friendly, and the first of its beginner examples lists Python basics as the prerequisite. The README also says its coverage of recent models may be a bit lacking in the state of the art. Who uses it, and how | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Self-study | Developers and students learning on their own | Fork the repository and follow the self-study steps on the Getting Started page, one lesson at a time | A learner takes the pre-lecture quiz for Lesson 03, runs the Perceptron notebook and then does its lab. | | Classrooms | Teachers running a course | Use the repository as it is and say which lesson the class works through, or use GitHub Classroom with a repository per lesson | A teacher assigns Lesson 07 and asks students to submit their quiz answers as issues. | | Software teams | Developers who call AI models but never studied how models learn | Read the neural network lessons, then the one on Transformers and BERT | A web developer reads Lessons 03 to 05 and Lesson 18 to see what sits behind the APIs the team uses. | | Local readers | Learners who want the lessons as a local website | Serve the lessons with Docsify, as the Getting Started page describes | A learner forks the repository, installs Docsify and runs docsify serve to read the lessons at localhost:3000. The repository’s index.html loads Docsify from cdn.jsdelivr.net, so it still needs a connection. | Skip it if you want the newest techniques or a business view of AI. The README leaves out business cases, classic machine learning, cloud ML services, chat bots and the deep mathematics, and names other places to learn them. ### Where to use it The curriculum is Markdown lessons, Jupyter notebooks and labs. The How to Run page says you need to run Python code in its Jupyter notebooks, and gives local, container and cloud routes. - Your own computer: Miniconda with the ai4beg environment, then VS Code or Jupyter in your browser - A dev container: the repository’s .devcontainer folder, which VS Code offers to reopen the code in - GitHub Codespaces: a browser-based VS Code started from the Code button - Binder: free cloud Jupyter, with some web access blocked and basic compute - A cloud GPU: a Data Science Virtual Machine, an Azure Machine Learning workspace or Google Colab - GitHub itself: read the text and look through the code without running anything The page says model training can be painfully slow without a GPU, so the later lessons are where a cloud GPU helps most. ### How to set it up Get it saves a zip of Mindket’s setup notes in your downloads folder, with the steps from Microsoft’s setup pages. The main route is a clone and a conda environment. The README tells you to fork the repository with the Fork button at the top right, and then clone it. What you need first | Need | Detail | | --- | --- | | Git | To clone the repository | | Miniconda | The How to Run page recommends it, for the conda package manager and its Python virtual environments | | VS Code | The page says the curriculum is best used in VS Code with the Python extension. Jupyter in your browser works too | | Python | The conda environment file lists python with no version. The examples page recommends 3.8 or higher for its standalone scripts | | A GPU | Optional. The page says some later lessons would greatly benefit from one | 1. Install Miniconda. 2. Clone the repository and create the environment, as below. 3. Open the folder in VS Code and accept the Python extension it suggests, or start Jupyter in the folder. 4. Open a lesson’s README, then its notebook. ```sh git clone http://github.com/microsoft/ai-for-beginners cd ai-for-beginners conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` The repository holds translations in 50+ languages, which makes a clone large. The README gives a sparse checkout that leaves them out. ```sh git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git cd AI-For-Beginners git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' ``` ### How to use it Start at Lesson 01 or anywhere you like. Take a lesson’s pre-lecture quiz, read its text and open its notebooks. In VS Code you open the .ipynb file. For Jupyter in the browser, start it in the course folder: ```sh jupyter notebook ``` Then open any .ipynb file and work in it. The README’s table lists no notebook or lab for Lesson 01. The lesson itself has a pre-lecture and a post-lecture quiz, a challenge and an assignment, Game Jam. Labs, where a lesson has one, sit in that lesson’s lab folder. ### Example You have an evening and a laptop with Python. You clone the repository and run the first beginner example from the examples folder: ```sh cd examples python 01-hello-ai-world.py ``` The script teaches a tiny model the rule y = 2x from five examples. It prints its progress every 20 rounds, then tests the model on four new numbers. The starting weight is random, so only the initial guess line changes from run to run. By round 20 the weight prints as 2.00 and all four tests match the true answer. Change learning\_rate (line 30 of the file) and run it again to see what training does. ### For newcomers and experts **If you are new.** A Jupyter notebook is a page that mixes text with code you run one cell at a time. conda keeps each project’s Python packages in their own environment, so this course’s packages do not clash with your other work. A neural network, the subject of Lessons 03 to 05, is a model that learns its settings from examples. Lesson 01 has a section on weak AI versus strong AI. If setup feels heavy, start with the four beginner examples, or read the lessons on GitHub with nothing installed. **For experts.** The environment file pins matplotlib 3.9, numpy 1.26, requests 2.32 and scipy 1.13. It takes PyTorch with torchtext, torchvision and torchdata from the pytorch channel and OpenCV from conda-forge. The root environment.yml reads the same as .devcontainer/environment.yml, and its pip step installs the requirements.txt beside it, which pins tensorflow 2.17.0, keras 3.15.0, gym 0.26.2 and tensorboard 2.17.1. Not every lesson has both frameworks. The README’s table lists TensorFlow only for Lessons 11 and 19, and PyTorch only for Lesson 20. Lesson 08’s TensorFlow link opens Lesson 05’s IntroKerasTF.ipynb. The dev container uses Microsoft’s Miniconda image and builds the same environment from environment.yml after it is created. The quiz app in etc/quiz-app is a separate npm project: npm install, then npm run serve. ### Good to know The notebooks run on your machine or on the cloud service you pick. The lessons’ quiz links open the online quiz app at ff-quizzes.netlify.app, unless you run the quiz app yourself. Some notebooks fetch models or datasets from the public internet when they run, and the How to Run page says Binder blocks some of that. - Cost: the How to Run page calls Binder free and says Google Colab comes with some free GPU support. It gives no prices for Codespaces, Azure VMs or Azure Machine Learning workspaces - Windows: if conda fails on gdk-pixbuf with a gbk codec error, set PYTHONUTF8 to 1 and try again, as the How to Run page says - Pages disagree: the Getting Started page says its quiz app holds 50 quizzes, and the quiz app README says 40. The README says 24 lessons, while its table also lists a 25th in Extras. Its online quiz link and the Getting Started page’s link point to different hosts - Age: large language models get one lesson in the table, Lesson 20, and the README says the course may lag the state of the art - Tracking links: the README’s Microsoft Learn links carry tracking IDs, so a click carries them - Removal: the pages give no uninstall steps. Deleting the folder is Mindket’s suggestion. The How to Run page removes the environment with conda deactivate, then conda env remove --name ai4beg - License: MIT. The LICENSE file reads “Copyright (c) Microsoft Corporation.” ## README ````markdown # AI For Beginners: setup notes This kit holds setup notes only. Mindket wrote them. The curriculum, its lessons, notebooks and labs stay in Microsoft's repository: https://github.com/microsoft/AI-For-Beginners These notes are not made or endorsed by Microsoft. The README titles the repository "Artificial Intelligence for Beginners - A Curriculum" and says: "Explore the world of Artificial Intelligence (AI) with our 12-week, 24-lesson curriculum! It includes practical lessons, quizzes, and labs. The curriculum is beginner-friendly and covers tools like TensorFlow and PyTorch, as well as ethics in AI". The notes were written from the README and the setup pages read on 3 November 2026. License: MIT. The LICENSE file (https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE) reads "Copyright (c) Microsoft Corporation." The README's Special Thanks section names Dmitry Soshnikov, PhD, as primary author, Jen Looper, PhD, as editor and Tomomi Imura as sketchnote illustrator. ## What the curriculum is It is Markdown lessons plus Jupyter notebooks and labs. The README says each lesson can contain pre-reading material, executable Jupyter notebooks, labs for some topics and links to Microsoft Learn modules. The notebooks "are often specific to the framework (PyTorch or TensorFlow)". The notebook also holds theory, so the README says to go through at least one version of it. Source: https://github.com/microsoft/AI-For-Beginners/blob/main/README.md The README says it will not cover business uses of AI, classic machine learning (it points to Microsoft's Machine Learning for Beginners curriculum), Cognitive Services, cloud ML frameworks, conversational AI and chat bots, or the deep mathematics behind deep learning. About the neural architectures, it says "We will cover recent models but may be a bit lacking in the state-of-the-art." ## The lessons Titles are copied from the README's Content table. The folder is under `lessons/`. A dash means the table lists nothing in that column for the lesson. | # | Lesson | Folder | Notebooks the table lists | Lab | |---|---|---|---|---| | 0 | Course Setup | 0-course-setup | Setup Your Development Environment (how-to-run.md) | - | | 01 | Introduction and History of AI | 1-Intro | - | - | | 02 | Knowledge Representation and Expert Systems | 2-Symbolic | Expert Systems, Ontology, Concept Graph | - | | 03 | Perceptron | 3-NeuralNetworks/03-Perceptron | one | yes | | 04 | Multi-Layered Perceptron and Creating our own Framework | 3-NeuralNetworks/04-OwnFramework | one | yes | | 05 | Intro to Frameworks (PyTorch/TensorFlow) and Overfitting | 3-NeuralNetworks/05-Frameworks | PyTorch, Keras, TensorFlow | yes | | 06 | Intro to Computer Vision. OpenCV | 4-ComputerVision/06-IntroCV | one | yes | | 07 | Convolutional Neural Networks & CNN Architectures | 4-ComputerVision/07-ConvNets | PyTorch, TensorFlow | yes | | 08 | Pre-trained Networks and Transfer Learning and Training Tricks | 4-ComputerVision/08-TransferLearning | PyTorch, TensorFlow (the TensorFlow link opens Lesson 05's IntroKerasTF.ipynb) | yes | | 09 | Autoencoders and VAEs | 4-ComputerVision/09-Autoencoders | PyTorch, TensorFlow | - | | 10 | Generative Adversarial Networks & Artistic Style Transfer | 4-ComputerVision/10-GANs | PyTorch, TensorFlow | - | | 11 | Object Detection | 4-ComputerVision/11-ObjectDetection | TensorFlow | yes | | 12 | Semantic Segmentation. U-Net | 4-ComputerVision/12-Segmentation | PyTorch, TensorFlow | - | | 13 | Text Representation. Bow/TF-IDF | 5-NLP/13-TextRep | PyTorch, TensorFlow | - | | 14 | Semantic word embeddings. Word2Vec and GloVe | 5-NLP/14-Embeddings | PyTorch, TensorFlow | - | | 15 | Language Modeling. Training your own embeddings | 5-NLP/15-LanguageModeling | PyTorch, TensorFlow | yes | | 16 | Recurrent Neural Networks | 5-NLP/16-RNN | PyTorch, TensorFlow | - | | 17 | Generative Recurrent Networks | 5-NLP/17-GenerativeNetworks | PyTorch, TensorFlow | yes | | 18 | Transformers. BERT. | 5-NLP/18-Transformers | PyTorch, TensorFlow | - | | 19 | Named Entity Recognition | 5-NLP/19-NER | TensorFlow | yes | | 20 | Large Language Models, Prompt Programming and Few-Shot Tasks | 5-NLP/20-LangModels | PyTorch | - | | 21 | Genetic Algorithms | 6-Other/21-GeneticAlgorithms | one | - | | 22 | Deep Reinforcement Learning | 6-Other/22-DeepRL | PyTorch, TensorFlow | yes | | 23 | Multi-Agent Systems | 6-Other/23-MultiagentSystems | - | - | | 24 | AI Ethics and Responsible AI | 7-Ethics | Microsoft Learn: Responsible AI Principles (a link, no notebook) | - | | 25 | Multi-Modal Networks, CLIP and VQGAN (Extras) | X-Extras/X1-MultiModal | one | - | The README groups them under Introduction to AI, Symbolic AI, Introduction to Neural Networks, Computer Vision, Natural Language Processing, Other AI Techniques, AI Ethics and Extras. Source: https://github.com/microsoft/AI-For-Beginners/blob/main/README.md ## Ways to run it - setup/local-conda.md: your own computer, with Miniconda, VS Code or Jupyter in the browser. - setup/dev-container-and-codespaces.md: the repository's `.devcontainer` folder, and GitHub Codespaces. - setup/binder-and-cloud-gpu.md: Binder, and the cloud GPU options for the later lessons. - setup/beginner-examples.md: the four small examples in the `examples` folder. - setup/study-plan-and-quizzes.md: the self-study steps, the quizzes, and reading it as a local site with Docsify. - setup/for-teachers.md: the For Educators page. ## Keep a copy small The README says the repository holds translations in 50+ languages, which "significantly increases the download size". It gives a sparse checkout that leaves them out. Source: https://github.com/microsoft/AI-For-Beginners/blob/main/README.md ```bash git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git cd AI-For-Beginners git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' ``` On Windows CMD, the README gives: ```cmd git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git cd AI-For-Beginners git sparse-checkout set --no-cone "/*" "!translations" "!translated_images" ``` ## Update or remove The pages read give no update or uninstall steps. The curriculum is a repository, so to update a clone you pull from it with Git, and to remove it you delete the folder. Those two lines are Mindket's, not Microsoft's. The How to Run page does show how to remove a conda environment: `conda deactivate`, then `conda env remove --name ai4beg`. ## Other courses by the same team The README lists more curricula from the same team. Among them, Machine Learning for Beginners is at https://github.com/Microsoft/ML-for-Beginners and Generative AI for Beginners is at https://github.com/microsoft/generative-ai-for-beginners ## Sources - README: https://github.com/microsoft/AI-For-Beginners/blob/main/README.md - Getting Started with this Curricula: https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/0-course-setup/setup.md - How to Run the Code: https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/0-course-setup/how-to-run.md - For Educators: https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/0-course-setup/for-teachers.md - Beginner-Friendly AI Examples: https://github.com/microsoft/AI-For-Beginners/blob/main/examples/README.md - Quizzes: https://github.com/microsoft/AI-For-Beginners/blob/main/etc/quiz-app/README.md - environment.yml: https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml - LICENSE: https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE ```` ## What is in it 7 files, 23 KB, downloaded as a zip. ```text README.md 7 KB setup/beginner-examples.md 3 KB setup/binder-and-cloud-gpu.md 2 KB setup/dev-container-and-codespaces.md 2 KB setup/for-teachers.md 2 KB setup/local-conda.md 4 KB setup/study-plan-and-quizzes.md 3 KB ``` ## Source [microsoft/AI-For-Beginners on GitHub](https://github.com/microsoft/AI-For-Beginners) ## Intro videos - [AI For Beginners intro video 1](https://www.youtube.com/watch?v=EZLJf4wNn9U) ## 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 For Beginners (this page) | 70,626 | — | — | MIT | — | | [AI Engineering from Scratch](https://mindket.com/p/a6a7d8db-f3a0-4bdb-8630-63f48ed03348) | 77,834 | — | — | MIT | — | | [Build a Large Language Model (From Scratch)](https://mindket.com/p/e8e3b000-ecf4-4beb-b7d2-6ecdd8cc10a0) | 107,934 | — | — | — | — | | [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. - [Hacker News · prhrb · 2023-06-13](https://news.ycombinator.com/item?id=36306877): Artificial Intelligence for Beginners - [Bluesky · Thomas Manders · 2026-08-24](https://bsky.app/profile/twcm73.bsky.social/post/3mtsxdkzg4c2f): @itbros.nl - De AI cursus uit aflevering 29 was iets minder breaking dan mevrouw Nicholson aangaf. Ze is namelijk uit juni 2024. Voor basiskennis niet verkeerd, maar 2 jaar is lang in AI-land. learn.microsoft.com/en-us/shows/... - NL alternatief: www.ai-cursus.nl - [X · @toorkich · 2026-11-09](https://x.com/toorkich/status/2119582161669939665): Want to learn Machine Learning from scratch? Microsoft’s 12-week, project-based course teaches classical ML with Scikit-learn. A practical starting point for anyone entering the world of AI. 🔗 https://t.co/fyTfCUk9t6 \#github \#machinelearning - [Hacker News · gits1225 · 2022-08-15](https://news.ycombinator.com/item?id=32468870): Microsoft's AI for Beginners - [Bluesky · Mr. Ånand · 2026-10-20](https://bsky.app/profile/astrodevil1.bsky.social/post/3mybiczkujl2q): Stage 2: Build with GenAI Generative AI for Beginners, Microsoft (21 lessons, 121k+ GitHub stars) github.com/microsoft/g... Why: prompts, RAG and function calling come before agents, not after. - [X · @jfcarpio · 2026-10-31](https://x.com/jfcarpio/status/2116627117366714432): -Microsoft offers an entire AI course for FREE- \* 12 weeks of lessons… \* 24 beginner-friendly lessons \* Hands-on coding exercises… \* Completely open-source\! “If you want to understand how AI actually works, this is a great place to start.” https://t.co/80Mt389dsC - [Hacker News · EntICOnc · 2023-11-21](https://news.ycombinator.com/item?id=38367698): Microsoft's Generative AI for Beginners - [Bluesky · Vikas Gupta · 2026-09-07](https://bsky.app/profile/vicky-grok.bsky.social/post/3muw4lcyfqz2m): → How generative AI works + practical tools 3️⃣ Introduction to Responsible AI → Ethics, safety + responsible AI use Microsoft 4️⃣ AI Skills for Work → Use AI tools to become more productive 5️⃣ Generative AI for Beginners → GenAI basics + prompt skills 6️⃣ Responsible AI - [X · @grant5407 · 2026-10-23](https://x.com/grant5407/status/2113440572157669556): 1/8 微软 AI-For-Beginners 中文路径：第一周怎么开始 微软在 GitHub 开源的 AI 入门课：12 周、24 节课，含课程、测验和实验，用 TensorFlow 和 PyTorch，也讲 AI 伦理。仓库自带简体中文 README，零基础可直接跟。 - [Hacker News · nonfamous · 2025-08-06](https://news.ycombinator.com/item?id=44806474): Open models by OpenAI: Try Microsoft's "Generative AI for Beginners" repo on GitHub. The early chapters in particular give a good grounding of LLM architecture without too many assumptions of background knowledge. The video version of the series is good too. - [Bluesky · Syed Ubada · 2026-08-20](https://bsky.app/profile/ub-codes-1cr.bsky.social/post/3mtjex5jwug27): 2️⃣ Generative AI for Beginners — Microsoft 🔗 learn.microsoft.com/en-us/shows/... ⏱ \~10-15 hrs \| 18 lessons (18 videos + code notebooks) \| No formal exam \| 100% Free \| Self-paced \| ⚠️ No certificate - [X · @HytidelLegend · 2026-10-10](https://x.com/HytidelLegend/status/2108739563883819136): 想学 AI，却不知道从哪开始？ 这 5 个 GitHub 仓库，分别对应不同的学习目标👇 1、microsoft/AI-For-Beginners 想先看懂 AI 全貌：从神经网络到图像、文本，有课程、测验和实验，可以先挑感兴趣的主题 https://t.co/EoMTm4ECtK 2、microsoft/ML-For-Beginners 想动手练机器学习：用 Scikit-learn 学回归、分类、聚类，配套作业和参考解答 https://t.co/vgNFmBCpYh 3、d2l-ai/d2l-zh… ## Get it - [Open microsoft/AI-For-Beginners on GitHub](https://github.com/microsoft/AI-For-Beginners) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/27b5a28e-0060-4e05-a39d-ddd64a5a3c55) - [Its page on Mindket](https://mindket.com/p/27b5a28e-0060-4e05-a39d-ddd64a5a3c55)