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https://github.com/Microsoft/AI-For-Beginners

12 Weeks, 24 Lessons, AI for All!
https://github.com/Microsoft/AI-For-Beginners
ai artificial-intelligence cnn computer-vision deep-learning gan machine-learning nlp rnn
Last synced: about 1 month ago
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12 Weeks, 24 Lessons, AI for All!
Host: GitHub
URL: https://github.com/Microsoft/AI-For-Beginners
Owner: microsoft
License: mit
Created: 2021-03-03T16:27:36.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2024-04-21T14:26:35.000Z (5 months ago)
Last Synced: 2024-05-15T12:46:48.773Z (4 months ago)
Topics: ai, artificial-intelligence, cnn, computer-vision, deep-learning, gan, machine-learning, nlp, rnn
Language: Jupyter Notebook
Homepage: https://microsoft.github.io/AI-For-Beginners/
Size: 80.7 MB
Stars: 31,623
Watchers: 393
Forks: 5,121
Open Issues: 79
Metadata Files:
- Readme: README.md
- License: LICENSE
- Security: SECURITY.md
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# Artificial Intelligence for Beginners - A Curriculum

|![ Sketchnote by [(@girlie_mac)](https://twitter.com/girlie_mac) ](./lessons/sketchnotes/ai-overview.png)|

|:---:|

| AI For Beginners - _Sketchnote by [@girlie_mac](https://twitter.com/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

## What you will learn

**[Mindmap of the Course](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)**

In this curriculum, you will learn:

* Different approaches to Artificial Intelligence, including the "good old" symbolic approach with **Knowledge Representation** and reasoning ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)).

* **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](http://Tensorflow.org) and [PyTorch](http://pytorch.org).

* **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](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)

* Business cases of using **AI in Business**. Consider taking [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) learning path on Microsoft Learn, or [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), developed in cooperation with [INSEAD](https://www.insead.edu/).

* **Classic Machine Learning**, which is well described in our [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners).

* Practical AI applications built using **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. For this, we recommend that you start with modules Microsoft Learn for [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [natural language processing](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generative AI with Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** and others.

* Specific ML **Cloud Frameworks**, such as [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), or [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Consider using [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) and [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) learning paths.

* **Conversational AI** and **Chat Bots**. There is a separate [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) learning path, and you can also refer to [this blog post](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) for more detail.

* **Deep Mathematics** behind deep learning. For this, we would recommend [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) by Ian Goodfellow, Yoshua Bengio and Aaron Courville, which is also available online at [https://www.deeplearningbook.org/](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](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) Learning Path.

# Content

|     |                                                                 Lesson Link                                                                  |                                           PyTorch/Keras/TensorFlow                                          | Lab                                                            |

| :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ |

| 0  |                                 [Course Setup](./lessons/0-course-setup/setup.md)                                 |                      [Setup Your Development Environment](./lessons/0-course-setup/how-to-run.md)                       |   |

| I  |               [**Introduction to AI**](./lessons/1-Intro/README.md)      | | |

| 01  |       [Introduction and History of AI](./lessons/1-Intro/README.md)       |           -                            | -  |

| II |              [**Symbolic AI**](./03-using-generative-ai-responsibly/README.md?WT.mc_id=academic-105485-koreyst)              |

| 02  |       [Knowledge Representation and Expert Systems](./lessons/2-Symbolic/README.md)       |            [Expert Systems](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) /  [Ontology](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) /[Concept Graph](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb)                             |  |

| III |                        [**Introduction to Neural Networks**](./lessons/3-NeuralNetworks/README.md) |||

| 03  |                [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md)                 |                       [Notebook](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb)                      | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md") |

| 04  |                   [Multi-Layered Perceptron and Creating our own Framework](./07-building-chat-applications/README.md?WT.mc_id=academic-105485-koreyst)                   |        [Notebook](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb)        | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) |

| 05  |            [Intro to Frameworks (PyTorch/TensorFlow) and Overfitting](./lessons/3-NeuralNetworks/05-Frameworks/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb)             | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) |

| IV  |            [**Computer Vision**](./lessons/4-ComputerVision/README.md)             | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Explore Computer Vision on Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) |

| 06  |            [Intro to Computer Vision. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md)             |           [Notebook](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb)         | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) |

| 07  |            [Convolutional Neural Networks](./lessons/4-ComputerVision/07-ConvNets/README.md) &  [CNN Architectures](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb)             | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) |

| 08  |            [Pre-trained Networks and Transfer Learning](./lessons/4-ComputerVision/08-TransferLearning/README.md) and [Training Tricks](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb)             | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) |

| 09  |            [Autoencoders and VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb)             |  |

| 10  |            [Generative Adversarial Networks & Artistic Style Transfer](./lessons/4-ComputerVision/10-GANs/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/10-GANs/GANTF.ipynb)             |  |

| 11  |            [Object Detection](./lessons/4-ComputerVision/11-ObjectDetection/README.md)             |         [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb)             | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) |

| 12  |            [Semantic Segmentation. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](hhttps://microsoft.github.io/AI-For-Beginners/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb)             |  |

| V  |            [**Natural Language Processing**](./lessons/5-NLP/README.md)             | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Explore Natural Language Processing on Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)|

| 13  |            [Text Representation. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb)             | |

| 14  |            [Semantic word embeddings. Word2Vec and GloVe](./lessons/5-NLP/14-Embeddings/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb)             |  |

| 15  |            [Language Modeling. Training your own embeddings](./lessons/5-NLP/15-LanguageModeling/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb)             | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) |

| 16  |            [Recurrent Neural Networks](./lessons/5-NLP/16-RNN/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb)             |  |

| 17  |            [Generative Recurrent Networks](./lessons/5-NLP/17-GenerativeNetworks/README.md)             |           [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.md) / [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.md)             | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) |

| 18  |            [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb)             |  |

| 19  |            [Named Entity Recognition](./lessons/5-NLP/19-NER/README.md)             |           [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb)             | [Lab](./lessons/5-NLP/19-NER/lab/README.md) |

| 20  |            [Large Language Models, Prompt Programming and Few-Shot Tasks](./lessons/5-NLP/20-LangModels/README.md)             |           [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | |

| VI |            **Other AI Techniques** || |

| 21  |            [Genetic Algorithms](./lessons/6-Other/21-GeneticAlgorithms/README.md)             |           [Notebook](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | |

| 22  |            [Deep Reinforcement Learning](./lessons/6-Other/22-DeepRL/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb)             | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) |

| 23  |            [Multi-Agent Systems](./lessons/6-Other/23-MultiagentSystems/README.md)             |  | |

| VII |            **AI Ethics** | | |

| 24  |            [AI Ethics and Responsible AI](./lessons/7-Ethics/README.md)             |           [Microsoft Learn: Responsible AI Principles](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | |

| IX  |            **Extras** | | |

| 25  |            [Multi-Modal Networks, CLIP and VQGAN](./lessons/X-Extras/X1-MultiModal/README.md)             |           [Notebook](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/X-Extras/X1-MultiModal/Clip.ipynb)    | |

## 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**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) modules that cover related topics.

## Getting Started

We have created a [setup lesson](/lessons/0-course-setup/setup.md) to help you with setting up your development environment. For Educators, we have created a [curricula setup lesson](/lessons/0-course-setup/for-teachers.md) for you too!

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

## Meet other Learners

Join our [official AI Discord server](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) to meet and network with other learners taking this course and get support.

## 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](http://soshnikov.com), PhD

* **🔥 Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD 

* **🎨 Sketchnote illustrator:** [Tomomi Imura](https://twitter.com/girlie_mac)

* **✅ Quiz Creator:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/)

* **🙏 Core Contributors:** [Evgenii Pishchik](https://github.com/Pe4enIks)

## Other Curricula

Our team produces other curricula! Check out:

* [Data Science for Beginners](https://aka.ms/ds4beginners)

* [**Version 2.0** Generative AI for Beginners](https://aka.ms/genai-beginners)

* [**NEW** Cybersecurity for Beginners](https://github.com/microsoft/Security-101??WT.mc_id=academic-96948-sayoung)

* [Web Dev for Beginners](https://aka.ms/webdev-beginners)

* [IoT for Beginners](https://aka.ms/iot-beginners)

* [Machine Learning for Beginners](https://aka.ms/ml4beginners)

* [XR Development for Beginners](https://aka.ms/xr-dev-for-beginners)

* [Mastering GitHub Copilot for AI Paired Programming](https://aka.ms/GitHubCopilotAI)