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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

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.

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：

4576 - Supervised Machine Learning: Regression and Classification 的 4600 个评论（共 6,053 个）

创建者 Raksha C C

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Aug 12, 2025

very nice course

创建者 Zineb H

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Aug 11, 2025

very good course

创建者 Bhavy R

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Jul 28, 2025

very good course

创建者 Rajvardhan S

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Jul 15, 2025

fantastic course

创建者 Omelian

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Jun 15, 2025

best course ever

创建者 Ilyas N

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Jun 11, 2025

very good course

创建者 AKSHAT K

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May 4, 2025

Very good course

创建者 Timileyin D

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Apr 14, 2025

Excellent course

创建者 Farhan A

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Apr 1, 2025

Excellent course

创建者 smartcast C

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Dec 28, 2024

Best course ever

创建者 Divya S

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Nov 16, 2024

excellent course

创建者 Aya B

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Nov 3, 2024

tres interessant

创建者 Antoine L N

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Oct 10, 2024

Excellent Course

创建者 Edward J D

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Oct 7, 2024

Very math heavy!

创建者 Kavya L

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Oct 7, 2024

very informative

创建者 Aryan S

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Oct 3, 2024

amazing concepts

创建者 Deep G

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Sep 22, 2024

Very good course

创建者 Ridmika.K.H

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Sep 22, 2024

Brilliant course

创建者 amirreza k

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Sep 16, 2024

Extremely useful

创建者 Prajwal A

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Sep 14, 2024

very good course

创建者 NAVJOT S

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Aug 31, 2024

Excellent Course

创建者 faris t

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Aug 29, 2024

Amazing course!!

创建者 Abubakar A

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Aug 27, 2024

simply loved it.

创建者 Hansika K

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Aug 20, 2024

excellent course

创建者 duy b

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Aug 18, 2024

not for beginner