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Learner Reviews & Feedback for Unsupervised Learning, Recommenders, Reinforcement Learning by DeepLearning.AI

4.9
stars
3,673 ratings

About the Course

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....

Top reviews

RD

Sep 16, 2022

great introduction to machine learning. I tried to self study before but it didn't work and thanks to this course I did understand now a bunch of things I cant wrap up my head with. Thank you for this

CL

Jun 30, 2024

Good pace and really well-designed for those who are total strangers to machine learning. I could follow along quite easily and looking forward to try out some of those algorithms on my own free time.

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526 - 550 of 590 Reviews for Unsupervised Learning, Recommenders, Reinforcement Learning

By Birhanu G

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Sep 19, 2022

wow!

By Shivanand V

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Oct 23, 2024

Nice

By Ulfah H M

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Oct 22, 2024

good

By Leonardo G A

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Jul 3, 2024

nice

By Nguyen T N

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Apr 12, 2024

Hard

By Md. S A

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Mar 12, 2024

Best

By Naiyan N

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Mar 5, 2024

good

By Ishaan

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Jan 1, 2024

best

By HAYAA T T A

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Dec 1, 2023

روعة

By Dini P U

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Oct 19, 2023

good

By DB T

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Sep 25, 2023

Good

By RUSSO F

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Aug 23, 2023

good

By ليان ع ع ا

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Jun 24, 2023

Good

By Wickly G

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Apr 11, 2023

good

By Angger M R

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Apr 8, 2023

good

By Ande R

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Feb 17, 2023

good

By Minakshi

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Nov 18, 2022

bhjn

By Tharun V

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Oct 31, 2022

good

By Jaber

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Sep 11, 2022

<3

By Ritesh K S

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Nov 21, 2023

NA

By ADITYA C (

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May 8, 2023

NA

By Hernán G

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Aug 31, 2023

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By Mahek D K V (

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Jul 27, 2023

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By Tony H

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Aug 15, 2024

Greate ML Specialization program. I really enjoy the teach style of Andrew Ng. I can tell he has lots of heart and really want to share everything he know. The first two course had good pace. The last course feel abit compact with lots of information being packed in. While it was missing the smaller steps optional labs to help student practice the concept on simpler problems. Instead, the course 3 only have the final labs, which is surprisingly quite difficult when being thrown into it with so many new things at once. Overall, great program! I really enjoy learning ML from Andew Ng as the foundation for DL and AI for my career change into the AI Specialization role/field. Thank you Andrew.

By Rawabi S Q A

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Apr 14, 2024

The "Supervised Machine Learning: Regression and Classification, Advanced Learning Algorithms, Unsupervised Learning, Recommenders, Reinforcement Learning" course offers a comprehensive and valuable learning experience. It covers a wide range of machine learning topics, including supervised learning, advanced algorithms, unsupervised learning, recommenders, and reinforcement learning. The course provides a solid foundation in theory and practical applications, making it suitable for both beginners and experienced practitioners.