GitHub weekly list first! Microsoft’s open source ml course for novices, 12000 star

Time:2021-10-24

[introduction]: Microsoft has opened a machine learning course for beginners.

GitHub weekly list first! Microsoft's open source ml course for novices, 12000 star

The course is very popular in GitHub, ranking first in the weekly list.

GitHub weekly list first! Microsoft's open source ml course for novices, 12000 star

brief introduction

Ml for beginners is an open-source introductory course for machine learning by Microsoft. It has a total of 25 courses with a time cycle of 12 weeks. The courses mainly use the scikit learn library. While learning this course, you can also understand the cultures around the world, because the technology in the course will be applied to data from many parts of the world.

Each class includes pre class and post class tests, written instructions for completing the course, solutions, assignments, etc. The course content is based on project construction, which can let you practice while learning theory, and help you maintain the motivation of learning.

GitHub weekly list first! Microsoft's open source ml course for novices, 12000 star

GitHub weekly list first! Microsoft's open source ml course for novices, 12000 star

GitHub weekly list first! Microsoft's open source ml course for novices, 12000 star

GitHub weekly list first! Microsoft's open source ml course for novices, 12000 star

The authors of the course are Jen looper, Stephen Howell, Francesca lazzeri, Tomomi Imura, Cassie breviu, Dmitry soshnikov, Chris noring, Ornella altunyan and Amy Boyd.

Each lesson includes the following:

  1. Draft notes
  2. Supplementary video
  3. Pre class warm-up test
  4. Written course
  5. How to build a project distribution guide
  6. Knowledge check
  7. Curriculum challenges
  8. Supplementary reading
  9. task
  10. After class test

The project address is:

https://github.com/microsoft/…

introduction

For learners

When using this tutorial, learners are advised to fork the warehouse and complete the exercises by themselves or in groups, start with the pre class quiz, read the lecture and complete the rest of the activities.

  • Start with the pre class quiz
  • Read the lecture and complete the activities, review and reflect on each knowledge check
  • Create a project by understanding the course and think independently before looking at the solution code
  • Take an after-school quiz
  • Complete the challenge
  • Complete the task
  • After completing the course group, visit the discussion board and update your pat progress. Pat is a progress assessment tool

For teachers

You can use this course in your class anytime, anywhere, and in GitHub through GitHub classroom. Through the fork project, a warehouse is created for each class, which means that each folder needs to be extracted into the warehouse separately. Detailed operation methods have been provided on the official website.

https://github.blog/2020-03-18-set-up-your-digital-classroom-with-github-classroom/

You can also use the repository as is instead of GitHub classroom. Online format (zoom, teams or others) can form a group discussion room for the test and guide students to help them prepare for learning. Then invite students to take the test and submit their answers at a specific time.

If you need a more private format, ask students to fork the course one by one to their own GitHub warehouse as a private repository and grant you access. They can then complete the tests and assignments in private and submit them through your questions in class.

content

In constructing the course, the author follows two teaching principles: ensure that it is based on project engineering practice and includes frequent tests.

By ensuring that the content is consistent with the project, the process will be more attractive to students and the retention of concepts will be strengthened. In addition, the low-risk test before class determines the students’ intention to learn a topic, and the second test after class further consolidates the knowledge. The course is flexible and interesting, and can be studied in whole or in part. These projects start small and become more and more complex by the end of the 12 week cycle. This course also includes a postscript on the practical application of machine learning, which can be used as a basis for additional credits or discussions.

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