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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：

3751 - Supervised Machine Learning: Regression and Classification 的 3775 个评论（共 6,053 个）

创建者 Aryan V

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Jun 1, 2023

Very informative and engaging course.

创建者 Aaryan D

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May 25, 2023

An awesome intro to Machine Learning!

创建者 Willian M

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May 6, 2023

Amazing training, highly recommended.

创建者 yufei

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Mar 30, 2023

helpful! A great thanks to Andrew NG!

创建者 Arthur B S

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Mar 20, 2023

Ótimo curso para quem está começando.

创建者 Samuel G

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Dec 26, 2022

Thank you, this is an amazing course.

创建者 Nikhil T

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Nov 20, 2022

easy to learn with the help of videos

创建者 Rene A D P

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Oct 10, 2022

Incredible resource for learning ai.

创建者 Ali I

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Sep 28, 2022

Thanks alot for your fruitful coures.

创建者 Talha K

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Sep 7, 2022

Concise. Efficient. Beginner Friendly

创建者 Mohamed O

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Aug 25, 2022

very good course, and well organized.

创建者 Kishore I

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Aug 15, 2022

Excellent introductory course on ML.

创建者 emirhan e

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Aug 7, 2022

Thanks for this precious oppurtinity.

创建者 Carlo D

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Jul 26, 2022

best intro course to machine learning

创建者 Anush A

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Jul 12, 2022

best course to learn machine learning

创建者 Daniel A

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Jul 11, 2022

Course very well executed. Thank you.

创建者 Sayed M

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Jul 10, 2022

Thank you all for this amazing Course

创建者 黄金

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Jul 6, 2022

The lab part of the course is amazing

创建者 Yuhao W

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Jul 3, 2022

better than before, with python coded

创建者 Babacar M

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Jul 1, 2022

Great professor and exellent content!

创建者 Henrik S

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Jun 27, 2022

It was everything I wanted it to be!

创建者 Deleted A

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Feb 16, 2026

It's the best ML course I have seen.

创建者 Jean M H F

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Jan 17, 2026

Very good explanation and intuitive.

创建者 Abdalla H

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Oct 22, 2025

Perfect machine learning explanation

创建者 Tanvir A F

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Aug 14, 2025

Very helpful to understand in depth.