7.6 KiB
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
/solutionsfolders 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-appfolder.
| Lesson Number | Project Name/Group | Concepts Taught | Learning Objectives | Linked Lesson | Author |
|---|---|---|---|---|---|
| 01 | Introduction | Introduction to Machine Learning | Learn the basic concepts behind Machine Learning | 📓 | Amy |
| 02 | Introduction | The History of Machine Learning | Learn the history underlying this field | 📓 | Amy |
| 03 | North American Pumpkin Prices 🎃 | Regression | Get started with Python and Scikit-Learn for Regression models | 📓 | Jen |
| 04 | North American Pumpkin Prices 🎃 | Regression | Visualize and clean data in preparation for ML | 📓 | Jen |
| 05 | North American Pumpkin Prices 🎃 | Regression | Build a Linear Regression model | 📓 | Jen |
| 06 | North American Pumpkin Prices 🎃 | Regression | Build a Logistic Regression model | 📓 | Jen |
| 23 | Future | The Ethics of Machine Learning | What are the important ethical issues apparent now and how will they impact the field going forward? | 📓 | |
| 24 | Future | The Future of Machine Learning | What are the important trends that will shape the future of ML? | 📓 |
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.