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Microsoft Developer Community Blog
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Machine Learning for Beginners, Curriculum

Chris_Noring's avatar
Chris_Noring
Icon for Microsoft rankMicrosoft
Jun 30, 2021

It is our very great pleasure to announce the release of a new, free, MIT-licensed open-source curriculum all about classic Machine Learning: Machine Learning for Beginners. Brought to you by a team of Azure Cloud Advocates and Program Managers, we hope to empower students of all ages to learn the basics of ML. Presuming no knowledge of ML, we offer a free 12-week, 24-lesson curriculum, plus a bonus 'postscript' lesson to help you dive into this amazing field.

If you liked our first curriculum, Web Dev for Beginners, you will love Machine Learning for Beginners!

 

Join us on a voyage!

Travel around the world in this themed semester-long self-study course as we look at ML topics through the lens of world cultures.

 

Our curricula are structured with a modified Project-Based pedagogy and include:

  • a pre-lesson warmup quiz
  • a written lesson
  • video
  • knowledge checks
  • a project to build
  • infographics, sketchnotes, and visuals
  • a challenge
  • an assignment
  • a post-lesson quiz
  • a 'PAT' (see below)
  • opportunities to deepen your knowledge on Microsoft Learn

 

Meet the team!

 

 

What will you learn?

 

The lessons are grouped so that you can deep-dive into various important aspects of classic ML. We start with an introduction to ML concepts, moving to its history, concepts of fairness in machine learning, and discussing the tools and techniques of the trade. We then move on to Regression, Classification, Clustering, Natural Language Processing, Time Series Forecasting, Reinforcement Learning, with two 'applied' lessons demonstrating how to use your models within web apps for inference. We end with a 'postscript' lesson listing "real-world" applications of ML, showing how these techniques are used "in the wild".

To make it easy for new learners to get started with ML, we built the content so that it can be used offline and so that the exercises can be completed using .ipynb notebooks within Visual Studio Code. Grab your datasets and let's go!

This curriculum is all about "classic Machine Learning", so we tackle these basic concepts for the most part using Scikit-learn, a library that helps demystify and explain these concepts. We don't discuss deep learning or neural networks in this ML curriculum, but please stay tuned as we release our AI for Beginners curriculum this Fall!

Travel with us to discover North American pumpkin market pricing (Regression), Pan-Asian cuisines (Classification), Nigerian musical tastes (Clustering), European Hotel Reviews (NLP), World electricity usage (Time Series) and the Russian story about Peter and the Wolf (Reinforcement Learning).

 

How to use this curriculum: meet PAT

 

This is a self-study course, but it works well in groups so consider finding study buddies and learning together. Warm up with a pre-lesson low-stakes quiz and work through the lessons and assignments together or solo. Test your knowledge with the post-lesson quiz.

New for this curriculum is the use of Progress Assessment Tools in the Discussion Board area. Once done with a lesson group, visit the Discussion Board and copy the template to a new Discussion using the "quote reply". Fill in your learnings in the self-reflection box and respond to other students in the repo. Let's learn together!

We are also open to PRs and Issue raising, following our Code of Conduct and templating systems. We hope the community will chip in with translations of the lessons, quizzes and assignments. Thank you for participating as we learn together.

 

A sneak peek

 

This curriculum is filled with a lot of art, created by our team. Take a look at this cool sketchnote created by @girlie_mac .

 

Without further ado, please meet Machine Learning For Beginners: A Curriculum!

 

You need to LEARN Python?

Here's our best recommendations from LEARN:

 

- https://docs.microsoft.com/en-us/learn/modules/intro-to-python/

- https://docs.microsoft.com/en-us/learn/paths/python-first-steps/

 

 

 

Published Jun 30, 2021
Version 1.0
web apps
  • lukemurraynz's avatar
    lukemurraynz
    Learn Expert
    Jul 11, 2021

    I could be wrong, Nilesh  - but I believe this course is 'Self Study', there's no need to register, just go to the course on Github and follow it through at your own space, the course seems to have links to various articles and videos to help you on the journey!

    It looks more like a Curriculum, so if you were a trainer, this would be some good material to develop a course around! However, there is still a lot of good content to learn off of yourself.

    Getting Started

    Students, to use this curriculum, fork the entire repo to your own GitHub account and complete the exercises on your own or with a group

  • priyeshdave21's avatar
    priyeshdave21
    Copper Contributor
    Jan 19, 2022

    Hi Chris_Noring ,

    Hope you are doing great.
    I am currently working on Career Guidance prediction using Machine Learning.

    The dataset has 38 features. For feature selection I tried using mutual_info_classif for getting the mutual information of my features and got the list of important features.

    The Second approach I followed is using SelectKBest with mutual_info_classif as my score_func. On this approach I got some other list of features.

    Is it normal to get different results ?
    Can you please help me out?

    https://lnkd.in/ghjj6PRB

     

    Ref : Line no. 98, 101

  • hrushi2000's avatar
    hrushi2000
    Copper Contributor
    Mar 21, 2022

    Machine learning is enabling computers to tackle tasks that have, until now, exclusively been administered by of us.

    From driving cars to translating speech, machine learning is driving academic degree explosion among the capabilities of computing – serving to package add of the untidy and unpredictable planet.

    But what specifically is machine learning and what is making the current boom in machine learning possible?

    WHAT IS MACHINE LEARNING?
    At a very high level, machine learning classes in Pune is that the strategy of teaching a information process system|ADP system|ADPS|system} the thanks to build correct predictions once fed information.

    Those predictions could also be responsive whether or not or not a touch of fruit {in a|during a|in academic degree exceedingly|in a very} photograph could also be a banana or associate apple, recognizing of us crossing the road before of a self-driving automobile, whether or not or not the use of the word book in an exceedingly} very sentence relates to a paperback or a building reservation, whether or not or not academic degree email is spam, or recognizing speech accurately enough to come back up with captions for a YouTube video.