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学生对 DeepLearning.AI 提供的 Unsupervised Learning, Recommenders, Reinforcement Learning 的评价和反馈

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5,340 个评分

课程概述

In the third course of the Machine Learning Specialization, you will: • Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection. • Build recommender systems with a collaborative filtering approach and a content-based deep learning method. • Build a deep reinforcement learning model. 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

ML

Dec 12, 2022

The whole specialisation is a great quick review of main topics. Proper learning requires deeper knowledge of algebra, calculus and python. But these courses are a fast, essential starting point.

ML

Aug 17, 2022

This course is an excellent course for introductory machine learning. All of the topics are covered in great detail and It is an honor to be taught by Andrew N.G, the Great teacher.

筛选依据：

476 - Unsupervised Learning, Recommenders, Reinforcement Learning 的 500 个评论（共 839 个）

创建者 Enock A

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Jun 20, 2025

Been so helpful at enhancing my skills

创建者 Ayush K

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Dec 8, 2024

This course is phenomenal for starters

创建者 Chu, Y

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Jul 22, 2024

Great course for unsupervised learning

创建者 Parsa N

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Jun 23, 2024

I love Reinforcement Learning section!

创建者 JUAN P P A

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Sep 9, 2023

Excellent content with the best mentor

创建者 Data i n B

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Jul 8, 2023

The best machine learning program ever

创建者 April 3

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Feb 22, 2023

Great course on reinforcement learning

创建者 Bishal G

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Oct 27, 2025

A must for machine learning learners!

创建者 Yakov K

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May 21, 2024

Amazing quality and clarity, as usual

创建者 Sol C P S

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Dec 13, 2024

The best online course I have taken!

创建者 Madhav M

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Sep 26, 2024

Thanks to all the team of Andrew NG.

创建者 hoanghero 1

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Mar 19, 2023

Easy to understand, useful knowledge

创建者 Mohammad T

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Aug 18, 2022

Awesome explanation of key concepts.

创建者 Shedrack M

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Nov 10, 2025

one of the best online courses ever

创建者 Fabio A T

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Nov 20, 2024

super Teacher and very good course.

创建者 Ayobami F

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Aug 23, 2024

Most educative. Thank you Coursera.

创建者 Jihan K

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Mar 26, 2023

informative, and easy to understand

创建者 Reza A

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Oct 18, 2022

It's great course. Thank you Andrew

创建者 srivatsav k

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Oct 21, 2024

Good for absolute beginners in ML.

创建者 Amos P

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Jan 11, 2025

Well structured and easy to learn

创建者 Wilmer R V

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Apr 19, 2024

Muy bueno, explicativo y práctico

创建者 Sohag H (

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Jan 8, 2024

learn a lot. love from Bangladesh

创建者 Xuesong T

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Jan 7, 2024

very useful course! Thank Andrew!

创建者 Boikhutso M

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Nov 29, 2023

Very well structured intro to ML,

创建者 Jeremy L

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Aug 6, 2022

Amazing course!

I learned a lot!