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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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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：

3726 - Supervised Machine Learning: Regression and Classification 的 3750 个评论（共 5,935 个）

创建者 Mohammad J S

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Dec 11, 2024

perfect course I have had this year.

创建者 Shaik M

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Jun 25, 2024

Andrew Ng is god of machine learning

创建者 Ashirvad P

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Jun 18, 2024

Good Course ! I personally enjoy it.

创建者 Gaurav P

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May 19, 2024

Really giving thorough understanding

创建者 Tanuj S

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Feb 12, 2024

Great course! Thank you to Andrew Ng

创建者 Jaroslaw K

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Jan 27, 2024

Easy to understand with helpful labs

创建者 Gokul A N

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Jan 13, 2024

One of best and correct paced course

创建者 Sahan L

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Jan 13, 2024

Excellent and very inspiring course.

创建者 Tanvir A S

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Dec 20, 2023

A wonderful course making ML easier.

创建者 Emre G

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Oct 9, 2023

amazing course, had amazing insights

创建者 Seyed K M Z

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Sep 7, 2023

a great course to start learning ML!

创建者 Ekins K

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Aug 4, 2023

Excellent course, beginner friendly.

创建者 Mahan S

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Jul 24, 2023

that was a great and helpful course.

创建者 hossein t

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Jul 6, 2023

andrew ng is really a great teacher.

创建者 Anaj P

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Jun 28, 2023

this course is ideal for ml beginner

创建者 Nawaraj K

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May 30, 2023

Great course and great instructor!!!

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