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

The course is popular on GitHub, ranking No. 1 on the weekly list.

Introduction to the

ML-For-Beginners is Microsoft’s open source introduction to machine learning. It consists of 25 lessons over a 12-week period. The course uses the Scikit-Learn library. While studying this course, you will also learn about cultures around the world, because the techniques in this course will be applied to data from many parts of the world.

Each session includes a pre-class and after-class quiz, written instructions for completing the lesson, solutions, assignments, etc. The course content is project-based and allows you to get your hands on the ground while studying theory, helping you to stay motivated.

The course was written by Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Ornella Altunyan and Amy Boyd.

Each session covers the following topics:

  1. The draft notes
  2. Add video
  3. A warm-up test before class
  4. The written curriculum
  5. How to build a distribution guide for a project
  6. Knowledge of inspection
  7. Course challenges
  8. Additional reading
  9. task
  10. Test after class

Project address is:

https://github.com/microsoft/…

An introduction to

For learners

When learners use the tutorial, it is recommended that they fork the warehouse and complete the exercises themselves or in small groups, starting with a pre-class quiz, reading the lecture, and completing the rest.

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

For educators

You can use the course anytime, anywhere in your own Classroom, and it’s available on GitHub Classroom through GitHub Classroom. Fork the project to create a repository for each lesson, which means you need to extract each folder into the repository separately. Detailed instructions are provided on the 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 formats (ZOOM, TEAMS, or others) can set up group discussion rooms for quizzes and mentor students to help them prepare for learning. The students are then invited to take a quiz and submit their answers at a set time.

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

content

In constructing the course, the authors followed two pedagogical principles: make sure it is based on the practice of project engineering, and include frequent tests.

By ensuring that the content is consistent with the project, the process is more attractive to students and retention of the concept is enhanced. In addition, a low-risk test before class establishes a student’s intention to learn about a topic, while a second test after class further solidifies knowledge. The course is flexible and interesting and can be studied in whole or in part. These projects start small and become increasingly complex by the end of the 12-week cycle. The course also includes an afterword on practical applications of machine learning, which can be used as the basis for additional credit or discussion.

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