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12 Weeks, 24 Lessons, AI for All!

GitHub license GitHub contributors GitHub issues GitHub pull-requests PRs Welcome

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Artificial Intelligence for Beginners - A Curriculum

 Sketchnote by (@girlie_mac)
AI For Beginners - Sketchnote by @girlie_mac

Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about Artificial Intelligence.

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 lack a little bit on 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:

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

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
3Perceptron Text NotebookLab
4 Multi-Layered Perceptron and Creating our own FrameworkTextNotebookLab
5 Intro to Frameworks (PyTorch/TensorFlow)
Overfitting
Text
Text
PyTorch Keras/TensorFlow Lab
IVComputer Vision AI Fundamentals: Explore Computer Vision
Microsoft Learn Module on Computer Vision PyTorch TensorFlow
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 Processing AI Fundamentals: Explore Natural Language Processing
Microsoft Learn Module on Natural Language PyTorch TensorFlow
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

Mindmap of the Course

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 notebooks (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.

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.

Are you a student?

Get started with the following resources:

  • Student Hub page In 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 Join a global community of student ambassadors, this could be your way into Microsoft.

Getting Started

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 notebook - read our instructions, and find more advice on how to do it in this blog post.

Note: Instructions on how to run the code in this curriculum

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 to "learn out loud".
  • Chat with other learners on Gitter or in Telegram channel.

For further study, we recommend following these Microsoft Learn modules and learning paths.

Teachers, we have included some suggestions on how to use this curriculum.


Credits

✍️ Primary Author: Dmitry Soshnikov, PhD
πŸ”₯ Editor: Jen Looper, PhD
🎨 Sketchnote illustrator: Tomomi Imura
βœ… Quiz Creator: Lateefah Bello, MLSA
πŸ™ Core Contributors: Evgenii Pishchik

Meet the Team

Promo video

πŸŽ₯ 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, Contributing, and Translation guidelines. Find our Support Documentation here and security information here. We welcome your constructive feedback!

A note about quizzes: All quizzes are contained in this app, 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 instruction in the etc/quiz-app folder.

Offline access

You can run this documentation offline by using Docsify. Fork this repo, install Docsify 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.

Help Wanted!

Would you like to contribute a translation? Please read our translation guidelines.

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