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Learner Reviews & Feedback for Deep Learning and Reinforcement Learning by IBM

4.6

stars

264 ratings

About the Course

This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future. After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Deep Learning and Reinforcement Learning. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics....

Top reviews

May 11, 2023

Complex concepts and techniques introduced in a very simple and comprehensive manner. Perfect intro to deep learning

Mar 6, 2023

Excellent course and beautiful eye opener for me! Five out of Five Stars!

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26 - 44 of 44 Reviews for Deep Learning and Reinforcement Learning

By Tim T

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Mar 6, 2023

Excellent course and beautiful eye opener for me! Five out of Five Stars!

By Gopala K M S

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Jan 29, 2025

The project in the end helped me get hands on experience.

By Abhishek K R

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Mar 10, 2025

finally completed this course...

By srinidhi B

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Nov 2, 2024

easy to uprgrade our skills

By Anya A

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Mar 9, 2025

Amazing Learning!

By Ling L Z

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Sep 2, 2025

Excellent!

By Victor M C

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Aug 19, 2024

buen Curso

By AHMED T

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Jul 15, 2025

merçi bcp

By Rahul C

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Aug 15, 2025

good

By Sushant B

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Sep 28, 2023

good

By Bachhar a

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Aug 19, 2023

good

By Bui V C

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Apr 19, 2025

Ibm

By Chakresh S

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May 10, 2023

The notebooks were really helpful. I suggest to include more mathematical lecturer in the course

By Subhadip C

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Jan 31, 2022

The core concepts of Deep Learning are explained well in this course.

By Bernard F

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Mar 18, 2021

Very good. I learned a lot but the subject matter is quite extensive.

By Arash Y

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Mar 8, 2025

Ich habe diese kurs längst erfolgreich abgeschlossen !!!

By Susan M

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Jan 20, 2023

inappropriate for my purposes. trying to unenroll..

By Shahribonu I

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Apr 18, 2024

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