Learn Machine Learning From Scratch – 12 Week And 24 Lessons

In this post, today I am going to explaining about Learn Machine Learning From Scratch12 Week And 24 Lessons.

Machine Learning for Beginners – A Curriculum

GitHub:

🌍 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. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming ‘AI for Beginners’ curriculum. Pair these lessons with our forthcoming ‘Data Science for Beginners’ curriculum, as well!

Travel with us around the world as we apply these classic 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 Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Ornella Altunyan, and Amy Boyd.

🎨 Thanks as well to our illustrators Tomomi Imura, Dasani Madipalli, and Jen Looper.

🙏 Special thanks 🙏 to our Microsoft Student Ambassador authors, reviewers and content contributors, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal.

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:

  • 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 running the solution code; however that code is available in the /solution folders in each project-oriented lesson.
  • Take the post-lecture quiz
  • Complete the challenge
  • Complete the assignment
  • After completing a lesson group, visit the Discussion board and “learn out loud” by filling out the appropriate PAT rubric. A ‘PAT’ is a Progress Assessment Tool that is a rubric you fill out to further your learning. You can also react to other PATs so we can learn together.

For further study, we recommend following these Microsoft Learn modules and learning paths.

Pedagogy:

We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on 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. This curriculum also includes a postscript on real-world applications of ML, which can be used as extra credit or as a basis for discussion.

Find our Code of ConductContributing, and Translation guidelines. We welcome your constructive feedback!

Each lesson includes:

  • optional sketchnote
  • optional supplemental video
  • pre-lecture 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-lecture quiz

A note about quizzes: All quizzes are contained in this app, for 50 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.

Lesson NumberTopicLesson GroupingLearning ObjectivesLinked LessonAuthor
01Introduction to machine learningIntroductionLearn the basic concepts behind machine learninglessonMuhammad
02The History of machine learningIntroductionLearn the history underlying this fieldlessonJen and Amy
03Fairness and machine learningIntroductionWhat are the important philosophical issues around fairness that students should consider when building and applying ML models?lessonTomomi
04Techniques for machine learningIntroductionWhat techniques do ML researchers use to build ML models?lessonChris and Jen
05Introduction to regressionRegressionGet started with Python and Scikit-learn for regression modelslessonJen
06North American pumpkin prices 🎃RegressionVisualize and clean data in preparation for MLlessonJen
07North American pumpkin prices 🎃RegressionBuild linear and polynomial regression modelslessonJen
08North American pumpkin prices 🎃RegressionBuild a logistic regression modellessonJen
09A Web App 🔌Web AppBuild a web app to use your trained modellessonJen
10Introduction to classificationClassificationClean, prep, and visualize your data; introduction to classificationlessonJen and Cassie
11Delicious Asian and Indian cuisines 🍜ClassificationIntroduction to classifierslessonJen and Cassie
12Delicious Asian and Indian cuisines 🍜ClassificationMore classifierslessonJen and Cassie
13Delicious Asian and Indian cuisines 🍜ClassificationBuild a recommender web app using your modellessonJen
14Introduction to clusteringClusteringClean, prep, and visualize your data; Introduction to clusteringlessonJen
15Exploring Nigerian Musical Tastes 🎧ClusteringExplore the K-Means clustering methodlessonJen
16Introduction to natural language processing ☕️Natural language processingLearn the basics about NLP by building a simple botlessonStephen
17Common NLP Tasks ☕️Natural language processingDeepen your NLP knowledge by understanding common tasks required when dealing with language structureslessonStephen
18Translation and sentiment analysis ♥️Natural language processingTranslation and sentiment analysis with Jane AustenlessonStephen
19Romantic hotels of Europe ♥️Natural language processingSentiment analysis with hotel reviews, 1lessonStephen
20Romantic hotels of Europe ♥️Natural language processingSentiment analysis with hotel reviews 2lessonStephen
21Introduction to time series forecastingTime seriesIntroduction to time series forecastinglessonFrancesca
22⚡️ World Power Usage ⚡️ – time series forecasting with ARIMATime seriesTime series forecasting with ARIMAlessonFrancesca
23Introduction to reinforcement learningReinforcement learningIntroduction to reinforcement learning with Q-LearninglessonDmitry
24Help Peter avoid the wolf! 🐺Reinforcement learningReinforcement learning GymlessonDmitry
PostscriptReal-World ML scenarios and applicationsML in the WildInteresting and revealing real-world applications of classical MLlessonTeam

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.

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Lingaraj Senapati
Hey There! I am Lingaraj Senapati, the Co-founder of lingarajtechhub.com My skills are Freelance, Web Developer & Designer, Corporate Trainer, Digital Marketer & Youtuber.
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