|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:
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.
|I||Introduction to AI|
|1||Introduction and History of AI||Text|
|2||Knowledge Representation and Expert Systems||Text||Expert System, Ontology, Concept Graph|
|III||Introduction to Neural Networks|
|4||Multi-Layered Perceptron and Creating our own Framework||Text||Notebook||Lab|
|5||Intro to Frameworks (PyTorch/TensorFlow)
|IV||Computer Vision||AI Fundamentals: Explore Computer Vision|
|Microsoft Learn Module on Computer Vision||PyTorch||TensorFlow|
|6||Intro to Computer Vision. OpenCV||Text||Notebook||Lab|
|7||Convolutional Neural Networks
|8||Pre-trained Networks and Transfer Learning
|9||Autoencoders and VAEs||Text||PyTorch||TensorFlow|
|10||Generative Adversarial Networks
Artistic Style Transfer
|12||Semantic Segmentation. U-Net||Text||PyTorch||TensorFlow|
|V||Natural Language Processing||AI Fundamentals: Explore Natural Language Processing|
|Microsoft Learn Module on Natural Language||PyTorch||TensorFlow|
|13||Text Representation. Bow/TF-IDF||Text||PyTorch||TensorFlow|
|14||Semantic word embeddings. Word2Vec and GloVe||Text||PyTorch||TensorFlow|
|15||Language Modeling. Training your own embeddings||Text||TensorFlow||Lab|
|16||Recurrent Neural Networks||Text||PyTorch||TensorFlow|
|17||Generative Recurrent Networks||Text||PyTorch||TensorFlow||Lab|
|19||Named Entity Recognition||Text||TensorFlow||Lab|
|20||Large Language Models, Prompt Programming and Few-Shot Tasks||Text||PyTorch|
|VI||Other AI Techniques|
|22||Deep Reinforcement Learning||Text||TensorFlow||Lab|
|24||AI Ethics and Responsible AI||Text||MS Learn: Responsible AI Principles|
|X1||Multi-Modal Networks, CLIP and VQGAN||Text||Notebook|
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.
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, and find more advice on how to do it in this blog post.
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:
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.
🎥Click the image above for a video about the project and the folks who created it!
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, 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
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.
Would you like to contribute a translation? Please read our translation guidelines.
Our team produces other curricula! Check out: