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

4.5

stars

182 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

Jan 15, 2023

Excellent Course with step by step instructions. Great for a neuro diverse person like me. Thank you course developers and the team for such a simple to follow logical course.

Apr 20, 2021

The concepts were clearly explained in lectures. The assignments were very helpful to gain a practical insight of the skills learned in the course.

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26 - 33 of 33 Reviews for Deep Learning and Reinforcement Learning

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 Lubna E K H

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Feb 15, 2023

there is no significant detail about RL

By Susan M

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Jan 20, 2023

inappropriate for my purposes. trying to unenroll..

By Khalid M

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Apr 28, 2023

The instructor just read the unclear slides

By Lokesh

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Dec 3, 2023

The jupyterlab does not work

By Shahribonu I

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Apr 18, 2024

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