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

4.9
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31,304 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

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.

DB

May 30, 2024

Great intro to supervised learning (regression & classification). Clear explanation of sigmoid function and decision thresholds. Could benefit from examples & exploring non-linear boundaries.

筛选依据：

5726 - Supervised Machine Learning: Regression and Classification 的 5750 个评论（共 5,930 个）

创建者 Kritagyay U

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Aug 28, 2023

The course structure is awesome .

创建者 Abhishek K

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Aug 11, 2023

Optional part should be explained

创建者 Amirhossein N

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May 26, 2023

thanks to Andrew NG. It was well.

创建者 20ECE053 M A I

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Sep 23, 2022

Really interesting, Good teacher

创建者 shubhankar

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Nov 13, 2025

good for fundaments and concepts

创建者 M B K

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Oct 13, 2025

very deeply and nicely explained

创建者 Ishit A

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Aug 21, 2023

great explanation for beginners.

创建者 L. M

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Nov 8, 2025

difficult but very informative.

创建者 Mohit Y

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Jun 9, 2023

I expected more rigrous course.

创建者 WONG, L H K

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May 23, 2023

No enough mathematical concepts

创建者 Ameya S

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Aug 23, 2024

Nicely explained for beginners

创建者 Kartik

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Sep 27, 2025

Needed more questions in quiz

创建者 SS K

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Jun 20, 2025

i like the way of teaching

创建者 M F R

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Aug 28, 2022

Very well organized course.

创建者 raj t

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Oct 6, 2024

very helpful for begginers

创建者 Zeyad M A

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Jul 23, 2024

Lacks end-to-end projects

创建者 Ritil R

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Jul 6, 2023

teach in a very basic way

创建者 F S

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Jun 30, 2023

need more code assignment

创建者 Manish P

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Oct 29, 2025

Good indepth into basics

创建者 Safwen S

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Jul 29, 2024

lacks practical examples

创建者 Amgad S a h

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Jul 11, 2023

explain more in the labs

创建者 Jean-Baptiste E

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Nov 7, 2025

good introduction to IA

创建者 Bharath K

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Apr 16, 2023

Course content was good