机器学习课程 机器学习课程为期 12 周、26 节课,在课程中你将了解经典机器学习的相关内容,主要使用 Scikit-learn 框架作为案例演示。 在机器学习型课程中,老师会提供一些数据集和案例。包括翻译、价格预测、情感分类等等,除此之外还会讲解一些基础知识,比如逻辑回归、聚类、序列模型、NLP等。
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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 traditional Machine Learning. In this lesson group, you will learn about what is sometimes called 'classic' ML, using primarily Scikit-Learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum.

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 Soshkinov, Amy Boyd, Ornella Altunyan

🙏Special thanks🙏 to our Microsoft Student Ambassador reviewers and content contributors, notably Rishit Dagli, Rohan Raj, Muhammad Sakib Khan Inan, 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:

  • 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
  • Consider forming a study group with friends to go through the content together.
  • For further study, we recommend following Microsoft Learn modules and learning paths.

Teachers, we have included some suggestions on how to use this curriculum.

Future space for Promo Video 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.

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 48 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 Number Section Concepts Taught Learning Objectives Linked Lesson Author
01 Introduction Introduction to Machine Learning Learn the basic concepts behind Machine Learning lesson Team
02 Introduction The History of Machine Learning Learn the history underlying this field lesson Jen and Amy
03 Introduction Fairness and Machine Learning What are the important philosophical issues around fairness that students should consider when building and applying ML models? lesson Tomomi
04 Introduction to Regression Regression Get started with Python and Scikit-Learn for Regression models lesson Jen
05 North American Pumpkin Prices 🎃 Regression Visualize and clean data in preparation for ML lesson Jen
06 North American Pumpkin Prices 🎃 Regression Build Linear and Polynomial Regression models lesson Jen
07 North American Pumpkin Prices 🎃 Regression Build a Logistic Regression model lesson Jen
08 A Web App 🔌 Web App Build a Web app to use your trained model lesson Jen
09 Introduction to Classification Classification Clean, Prep, and Visualize your Data; Introduction to Classification lesson Cassie
10 Delicious Asian Recipes 🍜 Classification Build a Discriminative Model lesson Cassie
11 Delicious Asian Recipes 🍜 Classification Build a Generative Model lesson Cassie
12 Delicious Asian Recipes 🍜 Classification Build a Web App using your Model lesson Jen
13 Introduction to Clustering Clustering Clean, Prep, and Visualize your Data; Introduction to Clustering lesson Jen
14 Exploring Nigerian Musical Tastes 🎧 Clustering Explore the K-Means Clustering Method lesson Jen
15 Introduction to Natural Language Processing Natural Language Processing Learn the basics about NLP by building a simple bot lesson Stephen
16 Common NLP Tasks Natural Language Processing Deepen your NLP knowledge by understanding common tasks required when dealing with language structures lesson Stephen
17 Translation and Sentiment Analysis ❤️ Natural Language Processing Translation and Sentiment analysis with Jane Austen lesson Stephen
18 Romantic Hotels of Europe ♥️ Natural Language Processing Sentiment analysis, continued lesson Stephen
19 Romantic Hotels of Europe ♥️ Natural Language Processing Sentiment analysis, continued lesson Stephen
20 Introduction to Time Series Forecasting Time Series Introduction to Time Series Forecasting lesson Francesca
21 World Power Usage Time Series Forecasting with ARIMA Time Series Time Series Forecasting with ARIMA lesson Francesca
22 Introduction to Reinforcement Learning Reinforcement Learning tbd lesson Dmitry
23 Help Peter avoid the Wolf! 🐺 Reinforcement Learning tbd lesson Dmitry
24 Real-World ML Scenarios and Applications ML in the Wild Interesting and Revealing real-world applications of classical ML lesson Team

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.