Machine Learning for Beginners - A Curriculum

🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍

Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about Machine Learning. Travel with us around the world as we apply these techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.

Hearty thanks to our authors (list all authors here)

Teachers, we have included some suggestions on how to use this curriculum. If you would like to create your own lessons, we have also included a lesson template

Students, to use this curriculum on your own, fork the entire repo and complete the exercises on your own:

• Start with a pre-lecture quiz
• Read the lecture and complete the activities, pausing and reflecting at each knowledge check.
• Try to create the projects by comprehending the lessons rather than copying the solution code; however that code is available in the /solutions folders in each project-oriented lesson.
• Take the post-lecture quiz
• Complete the challenge
• Complete the assignment
• Consider forming a study group with friends and go through the content together.
• For further study, we recommend Microsoft Learn and by watching the videos mentioned below.

Future space for 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 project-based and that it includes frequent quizzes. In addition, this curriculum has a common theme to give it cohesion.

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. We welcome your constructive feedback!

Each lesson includes:

• optional sketchnote
• optional supplemental video
• pre-lesson warmup quiz
• written lesson
• for project-based lessons, step-by-step guides on how to build the project
• knowledge checks
• a challenge
• supplemental reading
• assignment
• post-lesson quiz

A note about quizzes: All quizzes are contained [in this app](link a quiz app here), for 48 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 quiz-app folder.

	Project Name/Group	Concepts Taught	Learning Objectives	Linked Lesson	Written Lesson	Sketchnote	Assignment	Starting Quiz	Ending Quiz	Video	Author
01	Introduction	Introduction to Machine Learning	Learn the basic concepts behind Machine Learning	link							Author
02	Introduction	The History of Machine Learning	Learn the history underlying this field	link							Author
03	Introduction	The Ethics of Machine Learning	Learn the ethics that are important to understand when working in ML	link							Author
04	Tooling	Tools of the Trade	Get set up to become an ML Programmer	link							Author
05	Tooling	Programming for Machine Learning Tasks	Learn basic strategies for programming for ML, using Python and JavaScript	link							Author
06	Tooling	Math Basics for ML	Basic mathematics tasks useful for Machine Learning	link							Author
07	Tooling	Algorithms of Interest	Learn useful algorithms that you might use for various ML tasks	link							Author
08	Libraries	TensorFlow	Learn the basics of TensorFlow 2	link							Author
09	Libraries	PyTorch	Learn how to use PyTorch	link							Author
10	Libraries	Skikit-Learn	Learn about Skikit-Learn	link							Author
11	Libraries	Keras	Learn how to use Keras	link							Author
12	Libraries	Apache SparkML	Learn how to use Spark ML	link							Author
13	Libraries	NLTK	Learn how to use NLTK for Natural Language Processinng	link							Author
14	Types of ML	Supervised Learning 1	Learn about supervised learning	link							Author
15	Types of ML	Supervised Learning 2	Learn about supervised learning	link							Author
16	Types of ML	Unsupervised Learning 1	Learn about unsupervised learning	link							Author
17	Types of ML	Unsupervised Learning 2	Learn about unsupervised learning	link							Author
18	Types of ML	Reinforcement Learning 1	Learn about reinforcement learning	link							Author
19	Types of ML	Reinforcement Learning 2	Learn about reinforcement learning	link							Author
20	Applied ML	ML on Edge devices	Learn how to use ML in on edge devices such as Raspberry Pi	link							Author
21	Applied ML	ML on the Web	Learn about using ML on the web and in Progressive Web Apps	link							Author
22	Applied ML	ML for Mobile	Learn about using ML in mobile apps	link							Author
23	Applied ML	AutoML	Learn about AutoML, sometimes called 'ML for ML'	link							Author
24	Future	The Future of Machine Learning	What are the important trends that will shape the future of ML?	link							Author

Offline access

You can run this documentation offline by using Docsify. Fork this repo, install Docsify on your local machine, and then in the root folder of this repo, type docsify serve. The website will be served on port 3000 on your localhost: localhost:3000.