diff --git a/README.md b/README.md index 088d6d9c..4199dc9a 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,11 @@ |:---:| | AI For Beginners - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ | -Explore the world of **Artificial Intelligence** (AI) with Microsoft's 12-week, 24-lesson curriculum! Dive into Symbolic AI, Neural Networks, Computer Vision, Natural Language Processing, and more. Hands-on lessons, quizzes, and labs enhance your learning. Perfect for beginners, this comprehensive guide, designed by experts, covers TensorFlow, PyTorch, and ethical AI principles. Start your AI journey today!" +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: @@ -27,172 +31,94 @@ In this curriculum, you will learn: What we will not cover in this curriculum: -* 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-cacaste) learning path on Microsoft Learn, or [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-cacaste), developed in cooperation with [INSEAD](https://www.insead.edu/). +> [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-cacaste)**. 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-cacaste), [natural language processing](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-cacaste), **[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-cacaste), [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-cacaste). 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-cacaste) 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-cacaste) 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-cacaste) 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. +* 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-cacaste) Learning Path. +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. - -## Announcement - A New Curriculum on Generative AI was just released! - -We just released a 12-lesson curriculum on generative AI. Learn things like: - -- prompting and prompt engineering -- text and image app generation -- search apps - -As usual, there's a lesson, assignments to complete, knowledge checks, and challenges. - -Check it out: - -> https://aka.ms/genai-beginners - ---- # 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 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
NoLessonIntroPyTorchKeras/TensorFlowLab
IIntroduction to AI
1Introduction and History of AIText
IISymbolic AI
2 Knowledge Representation and Expert SystemsTextExpert System, Ontology, Concept Graph
IIIIntroduction to Neural Networks
3PerceptronText - NotebookLab
4 Multi-Layered Perceptron and Creating our own FrameworkTextNotebookLab
5Intro to Frameworks (PyTorch/TensorFlow) and OverfittingTextPyTorchKeras/TensorFlowLab
IVComputer VisionMicrosoft Azure AI Fundamentals: Explore Computer Vision
Microsoft Learn Module on Computer VisionPyTorchTensorFlow
6Intro to Computer Vision. OpenCVTextNotebookLab
7Convolutional Neural Networks
CNN Architectures		Text
Text		PyTorchTensorFlowLab
8Pre-trained Networks and Transfer Learning
Training Tricks		Text
Text		PyTorchTensorFlow
Dropout sample
Adversarial Cat		Lab
9Autoencoders and VAEsTextPyTorchTensorFlow
10Generative Adversarial Networks
Artistic Style Transfer		TextPyTorchTensorFlow GAN
Style Transfer
11Object DetectionTextPyTorchTensorFlowLab
12Semantic Segmentation. U-NetTextPyTorchTensorFlow
VNatural Language ProcessingMicrosoft Azure AI Fundamentals: Explore Natural Language Processing
Microsoft Learn Module on Natural language processingPyTorchTensorFlow
13Text Representation. Bow/TF-IDFTextPyTorchTensorFlow
14Semantic word embeddings. Word2Vec and GloVeTextPyTorchTensorFlow
15Language Modeling. Training your own embeddingsTextPyTorchTensorFlowLab
16Recurrent Neural NetworksTextPyTorchTensorFlow
17Generative Recurrent NetworksTextPyTorchTensorFlowLab
18Transformers. BERT.TextPyTorchTensorFlow
19Named Entity RecognitionTextTensorFlowLab
20Large Language Models, Prompt Programming and Few-Shot TasksTextPyTorch
VIOther AI Techniques
21Genetic AlgorithmsTextNotebook
22Deep Reinforcement LearningTextPyTorchTensorFlowLab
23Multi-Agent SystemsText
VIIAI Ethics
24AI Ethics and Responsible AITextMS Learn: Responsible AI Principles
Extras
X1Multi-Modal Networks, CLIP and VQGANTextNotebook
+* pre-reading material (linked as **Text**) +* 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. -**[Mindmap of the Course](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +## Getting Started -Each lesson contains some pre-reading material (linked as **Text** above), and some 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). There are also **Labs** available for some topics, which give you an opportunity to try applying the material you have learned to a specific problem. +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! -Some sections also contain links to **MS Learn** modules that cover related topics. Microsoft Learn provides a convenient GPU-enabled learning environment, although in terms of content you can expect this curriculum to go a bit deeper. +Don't forget to star (🌟) this repo to find it easier later. -# Are you a student? +## Meet other Learners -Get started with the following resources: +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. -- [Student Hub page](https://docs.microsoft.com/learn/student-hub?WT.mc_id=academic-77998-cacaste) On this page, you will find beginner resources, Student packs, and even ways to get a free cert voucher. This is one page you want to bookmark and check from time to time as we switch out content at least monthly. -- [Microsoft Student Learn ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77998-cacaste) Join a global community of student ambassadors, this could be your way into Microsoft. +## Help Wanted -# Getting Started +Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request -**Students**, there are a couple of ways to use the curriculum. First of all, you can just read the text and look through the code directly on GitHub. If you want to run the code in any of the notebooks - [read our instructions](./etc/how-to-run.md), and find more advice on how to do it [in this blog post](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). +## Special Thanks -> **Note**: [Instructions on how to run the code in this curriculum](./etc/how-to-run.md) - -However, if you would like to take the course as a self-study project, we suggest that you fork the entire repo to your own GitHub account and complete the exercises on your own or with a group: - -- Start with a pre-lecture quiz. -- Read the intro text for the lecture. -- If the lecture has additional notebooks, go through them, reading and executing the code. If both TensorFlow and PyTorch notebooks are provided, you can focus on one of them - choose your favorite framework. -- Notebooks often contain some of the challenges that require you to tweak the code a little bit to experiment. -- Take the post-lecture quiz. -- If there is a lab attached to the module - complete the assignment. -- Visit the [Discussion board](https://github.com/microsoft/AI-For-Beginners/discussions) to "learn out loud". - - -> For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/en-us/users/dmitrysoshnikov-9132/collections/31zgizg2p418yo/?WT.mc_id=academic-77998-cacaste) modules and learning paths. - -**Teachers**, we have [included some suggestions](./etc/for-teachers.md) on how to use this curriculum. - ---- - -## Credits - -**✍️ 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) - -## Meet the Team - -[![Promo video](/lessons/sketchnotes/ai-for-beginners.png)](https://youtu.be/m2KrAk0cC1c "Promo video") - -> 🎥 Click the image above for a video about the project and the folks who created it! - ---- - -## Pedagogy - -We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on **project-based** and that it includes **frequent quizzes**. - -By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12-week cycle. - -> Find our [Code of Conduct](etc/CODE_OF_CONDUCT.md), [Contributing](etc/CONTRIBUTING.md), and [Translation](etc/TRANSLATIONS.md) guidelines. Find our [Support Documentation here](etc/SUPPORT.md) and [security information here](etc/SECURITY.md). We welcome your constructive feedback! - -> **A note about quizzes**: All quizzes are contained [in this app](https://red-field-0a6ddfd03.1.azurestaticapps.net/), for 50 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instructions in the `etc/quiz-app` folder. - -## Offline access - -You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the `etc/docsify` folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`. A pdf of the curriculum is available [at this link](/etc/pdf/readme.pdf). - -## Help Wanted! - -Would you like to contribute a translation? Please read our [translation guidelines](etc/TRANSLATIONS.md). +* **✍️ 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: -- [AI for Beginners](https://aka.ms/ai-beginners) -- [Data Science for Beginners](https://aka.ms/datascience-beginners) -- [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/ml-beginners) -- [XR Development for Beginners](https://aka.ms/xr-dev-for-beginners) -- [Mastering GitHub Copilot for AI Paired Programming](https://aka.ms/GitHubCopilotAI) - +* [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) diff --git a/etc/for-teachers.md b/lessons/0-course-setup/for-teachers.md similarity index 97% rename from etc/for-teachers.md rename to lessons/0-course-setup/for-teachers.md index 44b3bc81..d57b0284 100644 --- a/etc/for-teachers.md +++ b/lessons/0-course-setup/for-teachers.md @@ -1,4 +1,4 @@ -## For Educators +# For Educators Would you like to use this curriculum in your classroom? Please feel free! @@ -18,6 +18,6 @@ If you prefer a more private format, ask your students to fork the curriculum, l There are many ways to make this work in an online classroom format. Please let us know what works best for you! -## Please give us your thoughts! +## Please give us your thoughts We want to make this curriculum work for you and your students. Please give us feedback in the discussion boards! diff --git a/etc/how-to-run.md b/lessons/0-course-setup/how-to-run.md similarity index 100% rename from etc/how-to-run.md rename to lessons/0-course-setup/how-to-run.md diff --git a/lessons/0-course-setup/setup.md b/lessons/0-course-setup/setup.md new file mode 100644 index 00000000..011ea8ed --- /dev/null +++ b/lessons/0-course-setup/setup.md @@ -0,0 +1,42 @@ +# Getting Started with this Curricula + +## Are you a student? + +Get started with the following resources: + +* [Student Hub page](https://docs.microsoft.com/learn/student-hub?WT.mc_id=academic-77998-cacaste) On this page, you will find beginner resources, Student packs, and even ways to get a free cert voucher. This is one page you want to bookmark and check from time to time as we switch out content at least monthly. +* [Microsoft Student Learn ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77998-cacaste) Join a global community of student ambassadors, this could be your way into Microsoft. + +**Students**, there are a couple of ways to use the curriculum. First of all, you can just read the text and look through the code directly on GitHub. If you want to run the code in any of the notebooks - [read our instructions](./etc/how-to-run.md), and find more advice on how to do it [in this blog post](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). + +> **Note**: [Instructions on how to run the code in this curriculum](/how-to-run.md) + +## Self Study + +However, if you would like to take the course as a self-study project, we suggest that you fork the entire repo to your own GitHub account and complete the exercises on your own or with a group: + +* Start with a pre-lecture quiz. +* Read the intro text for the lecture. +* If the lecture has additional notebooks, go through them, reading and executing the code. If both TensorFlow and PyTorch notebooks are provided, you can focus on one of them - choose your favorite framework. +* Notebooks often contain some of the challenges that require you to tweak the code a little bit to experiment. +* Take the post-lecture quiz. +* If there is a lab attached to the module - complete the assignment. +* Visit the [Discussion board](https://github.com/microsoft/AI-For-Beginners/discussions) to "learn out loud". + +> For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/en-us/users/dmitrysoshnikov-9132/collections/31zgizg2p418yo/?WT.mc_id=academic-77998-cacaste) modules and learning paths. + +**Teachers**, we have [included some suggestions](/for-teachers.md) on how to use this curriculum. + +--- + +## Pedagogy + +We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on **project-based** and that it includes **frequent quizzes**. + +By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12-week cycle. + +> **A note about quizzes**: All quizzes are contained [in this app](https://red-field-0a6ddfd03.1.azurestaticapps.net/), for 50 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instructions in the `etc/quiz-app` folder. + +## Offline access + +You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the `etc/docsify` folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`. A pdf of the curriculum is available [at this link](/etc/pdf/readme.pdf). \ No newline at end of file