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<!doctype html><html lang=en class=no-js> <head><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta name=description content=CS自学指南><meta name=author content=PKUFlyingPig><link href=https://csdiy.wiki/en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ML/ rel=canonical><link rel=icon href=../../../images/favicon16x16.png><meta name=generator content="mkdocs-1.3.1, mkdocs-material-7.3.6"><title>Coursera: Machine Learning - CSDIY.wiki</title><link rel=stylesheet href=../../../assets/stylesheets/main.a57b2b03.min.css><link rel=stylesheet href=../../../assets/stylesheets/palette.3f5d1f46.min.css><link rel=preconnect href=https://fonts.gstatic.com crossorigin><link rel=stylesheet href="https://fonts.googleapis.com/css?family=Roboto:300,400,400i,700%7CRoboto+Mono&display=fallback"><style>:root{--md-text-font-family:"Roboto";--md-code-font-family:"Roboto Mono"}</style><script>function 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</a> </li> <li class=md-nav__item> <a href=../../%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/ class=md-nav__link> CMU 10-708: Probabilistic Graphical Models </a> </li> <li class=md-nav__item> <a href=../../%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/ class=md-nav__link> Columbia STAT 8201: Deep Generative Models </a> </li> <li class=md-nav__item> <a href=../../%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/ class=md-nav__link> U Toronto STA 4273 Winter 2021: Minimizing Expectations </a> </li> <li class=md-nav__item> <a href=../../%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/ class=md-nav__link> Stanford STATS214 / CS229M: Machine Learning Theory </a> </li> </ul> </nav> </li> <li class=md-nav__item> <a href=../../%E5%90%8E%E8%AE%B0/ class=md-nav__link> Postscript </a> </li> </ul> </nav> </div> </div> </div> <div class="md-sidebar md-sidebar--secondary" data-md-component=sidebar data-md-type=toc> <div class=md-sidebar__scrollwrap> <div class=md-sidebar__inner> <nav class="md-nav md-nav--secondary" aria-label="Table of contents"> <label class=md-nav__title for=__toc> <span class="md-nav__icon md-icon"></span> Table of contents </label> <ul class=md-nav__list data-md-component=toc data-md-scrollfix> <li class=md-nav__item> <a href=#descriptions class=md-nav__link> Descriptions </a> </li> <li class=md-nav__item> <a href=#course-resources class=md-nav__link> Course Resources </a> </li> <li class=md-nav__item> <a href=#personal-resources class=md-nav__link> Personal Resources </a> </li> </ul> </nav> </div> </div> </div> <div class=md-content data-md-component=content> <article class="md-content__inner md-typeset"> <a href=https://github.com/pkuflyingpig/cs-self-learning/edit/master/docs/机器学习/ML.en.md title="Edit this page" class="md-content__button md-icon"> <svg xmlns=http://www.w3.org/2000/svg viewbox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg> </a> <h1 id=coursera-machine-learning>Coursera: Machine Learning</h1> <h2 id=descriptions>Descriptions</h2> <ul> <li>Offered by: Stanford</li> <li>Prerequisites: entry level of AI and proficient in Python</li> <li>Programming Languages: Python</li> <li>Difficulty: 🌟🌟🌟</li> <li>Class Hour: 100 hours</li> </ul> <p>When it comes to Andrew Ng, no one in the AI community should be unaware of him. He is one of the founders of the famous online education platform <a href=https://www.coursera.org>Coursera</a>, and also a famous professor at Stanford. This introductory machine learning course must be one of his famous works (the other is his deep learning course), and has hundreds of thousands of learners on Coursera (note that these are people who paid for the certificate, which costs several hundred dollars), and the number of nonpaying learners should be far more than that.</p> <p>The class is extremely friendly to novices, and Andrew has the ability to make machine learning as straightforward as 1+1=2. You'll learn about linear regression, logistic regression, support vector machines, unsupervised learning, dimensionality reduction, anomaly detection, and recommender systems, etc. and solidify your understanding with hands-on programming. The quality of the assignments needs no word to say. With detailed code frameworks and practical background, you can use what you've learned to solve real problems.</p> <p>Of course, as a public mooc, the difficulty of this course has been deliberately lowered, and many mathematical derivations are skimmed over. If you are interested in machine learning theory and want to investigate the mathematical theory behind these algorithms, you can refer to <a href=". /CS229.md">CS229</a> and <a href=". /CS189.md">CS189</a>.</p> <h2 id=course-resources>Course Resources</h2> <ul> <li>Course Website: <a href=https://www.coursera.org/learn/machine-learning>https://www.coursera.org/learn/machine-learning</a></li> <li>Recordings: refer to the course website</li> <li>Textbook: None</li> <li>Assignments: refer to the course website</li> </ul> <h2 id=personal-resources>Personal Resources</h2> <p>My implementation is lost in system reinstallation. However, the course is so famous that you can easily find related resoures online. Also, course material is available on Coursera.</p> <hr> <div class=md-source-date> <small> Last update: <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">September 22, 2022</span> </small> </div> <script src=https://giscus.app/client.js data-repo=PKUFlyingPig/cs-self-learning data-repo-id=R_kgDOGP67ng data-category=Announcements data-category-id=DIC_kwDOGP67ns4COM9Q data-mapping=title data-reactions-enabled=1 data-emit-metadata=0 data-input-position=top data-theme=light data-lang=zh-CN data-loading=lazy crossorigin=anonymous async>
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