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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：

3751 - Supervised Machine Learning: Regression and Classification 的 3775 个评论（共 6,080 个）

创建者 Nathan G

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Jul 13, 2024

Amazing! Really easy to follow along!

创建者 nachiket k

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Jul 7, 2024

one of the best teacher out there !!!

创建者 Jovie C

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May 19, 2024

very good course for machine learning

创建者 Diamond G

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Mar 24, 2024

Well explained and easy to understand

创建者 zahra m

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Mar 3, 2024

Great course for learning the basics!

创建者 Muhammad A

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Jan 19, 2024

History will never forget Andrew Sir.

创建者 Amos

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Jan 17, 2024

One of the best formats for learning!

创建者 Huong P

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Jan 7, 2024

easy to follow. very good instruction

创建者 Andres S

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Sep 21, 2023

Very didactic ! Perfect for beginners

创建者 Anuj J

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Sep 16, 2023

Must watch guide for machine learning

创建者 Benjamin M

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Aug 23, 2023

Very good course, I really recommend!

创建者 Zafiq

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Jul 31, 2023

Great insight on how the model works.

创建者 Aryan V

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Jun 1, 2023

Very informative and engaging course.

创建者 Aaryan D

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May 25, 2023

An awesome intro to Machine Learning!

创建者 Willian M

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May 6, 2023

Amazing training, highly recommended.

创建者 yufei

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Mar 30, 2023

helpful! A great thanks to Andrew NG!

创建者 Arthur B S

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Mar 20, 2023

Ótimo curso para quem está começando.

创建者 Samuel G

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Dec 26, 2022

Thank you, this is an amazing course.

创建者 Nikhil T

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Nov 20, 2022

easy to learn with the help of videos

创建者 Rene A D P

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Oct 10, 2022

Incredible resource for learning ai.

创建者 Ali I

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Sep 28, 2022

Thanks alot for your fruitful coures.

创建者 Talha K

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Sep 7, 2022

Concise. Efficient. Beginner Friendly

创建者 Mohamed O

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Aug 25, 2022

very good course, and well organized.

创建者 Kishore I

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Aug 15, 2022

Excellent introductory course on ML.

创建者 emirhan e

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Aug 7, 2022

Thanks for this precious oppurtinity.