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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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32,290 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

AP

Sep 3, 2022

A​ perfect course for beginners who are seeking to learn machine learning. I want to thank Mr. Andrew Ng and his team for this wonderful course. Thank you so much for providing this quality course.

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：

3701 - Supervised Machine Learning: Regression and Classification 的 3725 个评论（共 6,080 个）

创建者 soujan n

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Dec 25, 2024

Great insights for conceptualizing AI.

创建者 Nawar S

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Nov 17, 2024

The course very informative and clear.

创建者 Nour E

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Oct 24, 2024

Really thought through and informative

创建者 Imran R

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Sep 11, 2024

I have learn a lot of things from here

创建者 Racem D

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Aug 21, 2024

The BEST ML course I have taken so far

创建者 Alejandro G

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Jun 28, 2024

I learned a lot with a moderate effort

创建者 MD. M H

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Jun 12, 2024

Very nice and beginner friendly course

创建者 Toghrul I

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May 23, 2024

Best course for theoretical foundation

创建者 Leo R

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May 17, 2024

Very nice and very good explanations !

创建者 Robert R

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Apr 6, 2024

Amazing course. Extremely well done.

创建者 Yiqing W

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Apr 2, 2024

Informative, detailed and interesting.

创建者 Ming Y

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Mar 25, 2024

Thanks Andrew, really a good course ~!

创建者 vashu j

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Feb 9, 2024

very good if you can grab the concepts

创建者 Viswa R

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Feb 9, 2024

Loved the course very much. Wonderful.

创建者 Asd 4

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Feb 7, 2024

thank you Andrew Ng thank you Stanford

创建者 Abdullah A

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Sep 9, 2023

Perfect for beginners. Great course!!!

创建者 Varshney T

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Aug 26, 2023

best course i have ever studied from .

创建者 Sepehr M

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Aug 16, 2023

Was a good course to start the journey

创建者 Johanna D A

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Aug 4, 2023

excellent course, congratulations !!!!

创建者 Bikesh S

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Aug 4, 2023

great course to start maching learning

创建者 Subham G

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Jun 9, 2023

Very good course for Machine Learning!

创建者 Neha D

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May 31, 2023

Very Nice course.

Highly understandable

创建者 Yamil A

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May 18, 2023

very well content along of this course

创建者 Pedro P H

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May 10, 2023

Very clear, step by step explanations.

创建者 Shubham B

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Feb 5, 2023

This is exactly what I am looking for.