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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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30,502 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：

5551 - Supervised Machine Learning: Regression and Classification 的 5575 个评论（共 5,780 个）

创建者 Aadya G

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Nov 20, 2023

videos explaining the final code would be helpful

创建者 Konouz A

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Aug 5, 2023

really great, i wish tho there was more on coding

创建者 Devender S

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Jul 29, 2022

A well designed course to learn Machine Learning.

创建者 Ran S

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Jan 3, 2025

Detail and helpful with understanding the theory

创建者 Jerry Y

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Jul 14, 2023

Could have given vide walkthrough for the coding

创建者 Ali F

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Jun 5, 2023

A beginner level course. Great foundation topics

创建者 Tom P

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Aug 23, 2022

Would be nice to have more rigorous mathematics

创建者 Lucky V

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Dec 28, 2024

this course is superb, especially for beginners

创建者 LAVANYA D (

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Feb 5, 2024

Interesting course with interactive assignments

创建者 Chris O

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Oct 7, 2023

Really enlightening and helpful. Very educative

创建者 Samir A

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Aug 11, 2023

Best Machine Learning course for the beginner .

创建者 Riccardo D

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Nov 8, 2022

Graded exercises should in my opinion be harder

创建者 VISHVESH B (

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Sep 23, 2022

More practical knowledge should be focused on.

创建者 Ali R

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Aug 12, 2024

Great course to learn about Machine Learning.

创建者 Dainius K

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Feb 27, 2023

a bit too theoretical but it was interesting.

创建者 Mouad K

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Sep 15, 2024

More frequent labs would have helped me more

创建者 Kashif S

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Sep 20, 2023

The lab exercise should be more interactive.

创建者 Yellampalli S A

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Feb 22, 2024

Should make the Labs a bit more challenging

创建者 arshdeep s s

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Jun 16, 2023

week 3 is tough but this course is amazing

创建者 Saikat K

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May 19, 2023

The material and course structure was good

创建者 Mykola S

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Jun 24, 2022

a bit more complicated tests will be good

创建者 Bhanu P G S

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Dec 30, 2023

Codes re directly coded and not explained

创建者 TICHKULE R R

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May 18, 2025

little too outdated for today's scenario

创建者 Balasubbaiah L

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Nov 9, 2024

required more realtime usecase scenarios

创建者 Sabtain K

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Mar 25, 2024

Wish there were a bit more project work.