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Type Name Latest commit message Commit time .github disabling quiz auto-deploy Jun 17, 2021 1-Introduction tiny typo Jun 24, 2021 2-Regression reducing image size Jul 2, 2021 3-Web-App just a few typos Jun 22, 2021 4-Classification pinch image refresh Jun 28, 2021 5-Clustering Merge pull request #46 from softchris/clustering-2-k Jun 24, 2021 6-NLP a few clarifications so notebooks will run Jun 29, 2021 7-TimeSeries Merge pull request #50 from softchris/timeseries-intro Jun 25, 2021 8-Reinforcement escape edit for snowboard Jun 30, 2021 9-Real-World Add a sketchnote for Real world Jun 29, 2021 docs editing pdf Jun 23, 2021 pdf home page and pdf refresh Jun 29, 2021 quiz-app NLP 5 quiz 2 Jun 25, 2021 sketchnotes Edit readme Jul 2, 2021 .gitignore Edit graphic Jun 3, 2021 .nojekyll Initial commit Feb 5, 2021 CODE_OF_CONDUCT.md Initial CODE_OF_CONDUCT.md commit Mar 3, 2021 CONTRIBUTING.md Initial commit Feb 5, 2021 LICENSE Initial LICENSE commit Mar 3, 2021 README.md Fix Discussions link Jul 2, 2021 SECURITY.md links to Learn added Jun 9, 2021 SUPPORT.md support edits Jun 3, 2021 TRANSLATIONS.md update translation instructions Jul 1, 2021 docsifytopdf.js pdf refresh Jun 18, 2021 for-teachers.md Initial commit Feb 5, 2021 index.html edit to index.html for proper repo address May 27, 2021 ml-for-beginners.png home page and pdf refresh Jun 29, 2021 package-lock.json docsify to pdf Jun 17, 2021 package.json docsify to pdf Jun 17, 2021 View code Machine Learning for Beginners - A Curriculum Getting Started Meet the Team Pedagogy Each lesson includes: Offline access PDFs Help Wanted! Other Curricula README.md GitHub license GitHub contributors GitHub issues GitHub pull-requests PRs Welcome GitHub watchers GitHub forks GitHub stars 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. 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. Teachers, we have included some suggestions on how to use this curriculum. --------------------------------------------------------------------- Meet the Team 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 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 Conduct, Contributing, 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 Topic Lesson Learning Linked Author Number Grouping Objectives Lesson Introduction Learn the 01 to machine Introduction basic concepts lesson Muhammad learning behind machine learning The History of Learn the 02 machine Introduction history lesson Jen and learning underlying Amy this field What are the important philosophical issues around Fairness and fairness that 03 machine Introduction students lesson Tomomi learning should consider when building and applying ML models? What Techniques for techniques do Chris and 04 machine Introduction ML researchers lesson Jen learning use to build ML models? Get started with Python 05 Introduction Regression and lesson Jen to regression Scikit-learn for regression models North American Visualize and 06 pumpkin prices Regression clean data in lesson Jen preparation for ML North American Build linear 07 pumpkin prices Regression and polynomial lesson Jen regression models North American Build a 08 pumpkin prices Regression logistic lesson Jen regression model Build a web 09 A Web App Web App app to use lesson Jen your trained model Clean, prep, Introduction and visualize 10 to Classification your data; lesson Jen and classification introduction Cassie to classification Delicious 11 Asian and Classification Introduction lesson Jen and Indian to classifiers Cassie cuisines Delicious 12 Asian and Classification More lesson Jen and Indian classifiers Cassie cuisines Delicious Build a 13 Asian and Classification recommender lesson Jen Indian web app using cuisines your model Clean, prep, Introduction and visualize 14 to clustering Clustering your data; lesson Jen Introduction to clustering Exploring Explore the 15 Nigerian Clustering K-Means lesson Jen Musical Tastes clustering method Introduction Learn the to natural Natural basics about 16 language language NLP by lesson Stephen processing [?][?] processing building a simple bot Deepen your NLP knowledge by Common NLP Natural understanding 17 Tasks [?][?] language common tasks lesson Stephen processing required when dealing with language structures Translation Natural Translation 18 and sentiment language and sentiment lesson Stephen analysis [?] processing analysis with Jane Austen Romantic Natural Sentiment 19 hotels of language analysis with lesson Stephen Europe [?] processing hotel reviews, 1 Romantic Natural Sentiment 20 hotels of language analysis with lesson Stephen Europe [?] processing hotel reviews 2 Introduction Introduction 21 to time series Time series to time series lesson Francesca forecasting forecasting [?][?] World Power Usage [?][?] - Time series 22 time series Time series forecasting lesson Francesca forecasting with ARIMA with ARIMA Introduction Introduction to Reinforcement to 23 reinforcement learning reinforcement lesson Dmitry learning learning with Q-Learning Help Peter Reinforcement Reinforcement 24 avoid the learning learning Gym lesson Dmitry wolf! Interesting Real-World ML and revealing Postscript scenarios and ML in the Wild real-world lesson Team applications applications of classical 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. PDFs Find a pdf of the curriculum with links here Help Wanted! Would you like to contribute a translation? Please read our translation guidelines and add input here Other Curricula Our team produces other curricula! Check out: * Web Dev for Beginners About 12 weeks, 24 lessons, classic Machine Learning for all aka.ms/ml-beginners Topics python data-science machine-learning scikit-learn machine-learning-algorithms ml machinelearning machinelearning-python scikit-learn-python Resources Readme License MIT License Releases No releases published Packages 0 No packages published Contributors 13 * @jlooper * @softchris * @girliemac * @ornelladotcom * @stephen-howell * @microsoftopensource * @shwars * @dasani-madipalli * @digitarald * @abrookins * @microsoft-github-operations + 2 contributors Languages * Jupyter Notebook 99.4% * Other 0.6% * (c) 2021 GitHub, Inc. * Terms * Privacy * Security * Status * Docs * Contact GitHub * Pricing * API * Training * Blog * About You can't perform that action at this time. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session.