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学生对 DeepLearning.AI 提供的 Supervised Machine Learning: Regression and Classification 的评价和反馈

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32,078 个评分

课程概述

In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start....

热门审阅

MA

Jan 27, 2025

I've really enjoyed learning about Machine Learning in such a guided way. It will continue to inspire me to learn more about AI. Thank you Andrew Ng, DeepLearning.AI, Standford ONLINE, and Coursera.

AA

Apr 29, 2023

Optional Lab lot more time than mentioned without prior experience of python and libraries used. Its estimated time should be change, it's a lot more than 1 hour. Video and exercises are very good.

筛选依据：

3801 - Supervised Machine Learning: Regression and Classification 的 3825 个评论（共 6,053 个）

创建者 Thomas T

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May 10, 2023

Best ML course available in Coursera

创建者 Sharif M

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May 4, 2023

Very Simple and informative course

创建者 Justin H

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Feb 11, 2023

Andrew Ng == champion ML instruction

创建者 Ruttanan R

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Dec 26, 2022

Andrew Ng is a really great teacher!

创建者 Xiaodie L

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Nov 3, 2022

Easily understand and useful course!

创建者 Velizar T

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Oct 9, 2022

Great explanations and lots of fun!

创建者 Jeffrey S

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Aug 28, 2022

Excellent Course, Highly recommended

创建者 Yiğit T

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Aug 17, 2022

Very easy to understand, great job.

创建者 Fateh M

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Jul 31, 2022

Perfect, as expected from Andrew. Ng

创建者 Md. R H

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Jul 10, 2022

Very effective and wonderful course.

创建者 Banriskhem K

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Jul 9, 2022

Programming assignments are too easy

创建者 sohrab

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Dec 26, 2025

That was an amazing course indeed !

创建者 Gabriel C

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Sep 16, 2025

Excelente curso, muy bien explicado

创建者 Haraprasad S

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Sep 9, 2025

Awesome content and well presented.

创建者 Sergii Z

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Jul 26, 2025

It's a classic course for a reason!

创建者 Govind B S

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Jul 3, 2025

Excellent teaching by Andrew Ng sir

创建者 Roybert A

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Mar 15, 2025

Felicitaciones, excelente material.

创建者 Partha P D

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Jan 12, 2025

Informative and very comprehensive.

创建者 Wenhao L

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Jan 6, 2025

it is very good course for beginner

创建者 Fabio A T

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Nov 20, 2024

super Teacher and very good course.

创建者 Adeel S

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Nov 19, 2024

One of the best course I ever done.

创建者 Fazliddin S

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Oct 20, 2024

having kernel methods would be nice

创建者 Juan D C

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Sep 16, 2024

Great Intro and practical examples.

创建者 El H A S

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Jul 20, 2024

Really nice course, thanks Andrew^^

创建者 Nikhil R

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Jul 11, 2024

Learnt A Lot......Thanks Andrew sir