Artificial Intelligence for Beginners - A Curriculum


AI For Beginners - Sketchnote by @girlie_mac

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

🌐 Multi-Language Support

Supported via GitHub Action (Automated & Always Up-to-Date)

Prefer to Clone Locally?

This repository includes 50+ language translations which significantly increases the download size. To clone without translations, use sparse checkout:

Bash / macOS / Linux:
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'
CMD (Windows):
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"
This gives you everything you need to complete the course with a much faster download.

If you wish to have additional translations languages supported are listed here

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What you will learn

Mindmap of the Course

In this curriculum, you will learn:

Different approaches to Artificial Intelligence, including the “good old” symbolic approach with Knowledge Representation and reasoning (GOFAI).
Neural Networks and Deep Learning, which are at the core of modern AI. We will illustrate the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch.
Neural Architectures for working with images and text. We will cover recent models but may be a bit lacking in the state-of-the-art.
Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems.

What we will not cover in this curriculum:

Find all additional resources for this course in our Microsoft Learn collection

Business cases of using AI in Business. Consider taking Introduction to AI for business users learning path on Microsoft Learn, or AI Business School, developed in cooperation with INSEAD.
Classic Machine Learning, which is well described in our Machine Learning for Beginners Curriculum.
Practical AI applications built using Cognitive Services. For this, we recommend that you start with modules Microsoft Learn for vision, natural language processing, Generative AI with Azure OpenAI Service and others.
Specific ML Cloud Frameworks, such as Azure Machine Learning, Microsoft Fabric, or Azure Databricks. Consider using Build and operate machine learning solutions with Azure Machine Learning and Build and Operate Machine Learning Solutions with Azure Databricks learning paths.
Conversational AI and Chat Bots. There is a separate Create conversational AI solutions learning path, and you can also refer to this blog post for more detail.
Deep Mathematics behind deep learning. For this, we would recommend Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, which is also available online at https://www.deeplearningbook.org/.

For a gentle introduction to AI in the Cloud topics you may consider taking the Get started with artificial intelligence on Azure Learning Path.

Content

	Lesson Link	PyTorch/Keras/TensorFlow	Lab
0	Course Setup	Setup Your Development Environment
I	Introduction to AI
01	Introduction and History of AI	-	-
II	Symbolic AI
02	Knowledge Representation and Expert Systems	Expert Systems / Ontology /Concept Graph
III	Introduction to Neural Networks
03	Perceptron	Notebook	Lab
04	Multi-Layered Perceptron and Creating our own Framework	Notebook	Lab
05	Intro to Frameworks (PyTorch/TensorFlow) and Overfitting	PyTorch / Keras / TensorFlow	Lab
IV	Computer Vision	PyTorch / TensorFlow	Explore Computer Vision on Microsoft Azure
06	Intro to Computer Vision. OpenCV	Notebook	Lab
07	Convolutional Neural Networks & CNN Architectures	PyTorch /TensorFlow	Lab
08	Pre-trained Networks and Transfer Learning and Training Tricks	PyTorch / TensorFlow	Lab
09	Autoencoders and VAEs	PyTorch / TensorFlow
10	Generative Adversarial Networks & Artistic Style Transfer	PyTorch / TensorFlow
11	Object Detection	TensorFlow	Lab
12	Semantic Segmentation. U-Net	PyTorch / TensorFlow
V	Natural Language Processing	PyTorch /TensorFlow	Explore Natural Language Processing on Microsoft Azure
13	Text Representation. Bow/TF-IDF	PyTorch / TensorFlow
14	Semantic word embeddings. Word2Vec and GloVe	PyTorch / TensorFlow
15	Language Modeling. Training your own embeddings	PyTorch / TensorFlow	Lab
16	Recurrent Neural Networks	PyTorch / TensorFlow
17	Generative Recurrent Networks	PyTorch / TensorFlow	Lab
18	Transformers. BERT.	PyTorch /TensorFlow
19	Named Entity Recognition	TensorFlow	Lab
20	Large Language Models, Prompt Programming and Few-Shot Tasks	PyTorch
VI	Other AI Techniques
21	Genetic Algorithms	Notebook
22	Deep Reinforcement Learning	PyTorch /TensorFlow	Lab
23	Multi-Agent Systems
VII	AI Ethics
24	AI Ethics and Responsible AI	Microsoft Learn: Responsible AI Principles
IX	Extras
25	Multi-Modal Networks, CLIP and VQGAN	Notebook

Each lesson contains

Pre-reading material
Executable Jupyter Notebooks, which are often specific to the framework (PyTorch or TensorFlow). The executable notebook also contains a lot of theoretical material, so to understand the topic you need to go through at least one version of the notebook (either PyTorch or TensorFlow).
Labs available for some topics, which give you an opportunity to try applying the material you have learned to a specific problem.
Some sections contain links to MS Learn modules that cover related topics.

Getting Started

🎯 New to AI? Start Here!

If you’re completely new to AI and want quick, hands-on examples, check out our Beginner-Friendly Examples! These include:

🌟 Hello AI World - Your first AI program (pattern recognition)
🧠 Simple Neural Network - Build a neural network from scratch
🖼️ Image Classifier - Classify images with detailed comments
💬 Text Sentiment - Analyze positive/negative text

These examples are designed to help you understand AI concepts before diving into the full curriculum.

📚 Full Curriculum Setup

We have created a setup lesson to help you with setting up your development environment. - For Educators, we have created a curricula setup lesson for you too!
How to Run the code in a VSCode or a Codespace

Follow these steps:

Fork the Repository: Click on the “Fork” button at the top-right corner of this page.

Clone the Repository: git clone https://github.com/microsoft/AI-For-Beginners.git

Don’t forget to star (🌟) this repo to find it easier later.

Meet other Learners

Join our official AI Discord server to meet and network with other learners taking this course and get support.

If you have product feedback or questions whilst building visit our Azure AI Foundry Developer Forum

Quizzes

A note about quizzes: All quizzes are contained in the Quiz-app folder in etc\quiz-app, or Online Here They are linked from within the lessons the quiz app can be run locally or deployed to Azure; follow the instruction in the quiz-app folder. They are gradually being localized.

Help Wanted

Do you have suggestions or found spelling or code errors? Raise an issue or create a pull request.

Special Thanks

✍️ Primary Author: Dmitry Soshnikov, PhD
🔥 Editor: Jen Looper, PhD
🎨 Sketchnote illustrator: Tomomi Imura
✅ Quiz Creator: Lateefah Bello, MLSA
🙏 Core Contributors: Evgenii Pishchik

Other Curricula

Our team produces other curricula! Check out:

LangChain

Azure / Edge / MCP / Agents

Generative AI Series

Core Learning

Copilot Series

Getting Help

If you get stuck or have any questions about building AI apps. Join fellow learners and experienced developers in discussions about MCP. It’s a supportive community where questions are welcome and knowledge is shared freely.

If you have product feedback or errors while building visit: