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

4.6
stars
29 ratings
8 reviews

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

YE
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.

JM
Feb 8, 2021

Hello, thank you again for the course. My congrats, once more, to the instructor on the videos!

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1 - 8 of 8 Reviews for Deep Learning and Reinforcement Learning

By Yasar A

Apr 21, 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.

By Jose M

Feb 9, 2021

Hello, thank you again for the course. My congrats, once more, to the instructor on the videos!

By My B

Apr 30, 2021

The difficult terms are simplified enough for understanding and application in real life.

By Neha M

Mar 29, 2021

Excellent course

By Ashish P

Mar 29, 2021

Well prepared, gives a good intro to multiple Deep Learning algorithms and good examples to cover the major topics. A few more practice labs on CNN and RNN would have been awesome!

Cons : The only difficulty I found was with the english accent of our dear trainer. Sometimes it was really very difficult to comprehend what was being said and one needed to rewind the video multiple times and read the subtitles. Other than that, nothing to complain.

Cheers!

By Seif M M

Jan 12, 2021

Reinforcement Learning part needs to be a separate course and more details in it

By Bernard F

Mar 18, 2021

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

By Gideon D

Apr 24, 2021

good course, PROS: very well presented, clear amd methodic. appropriate tasks. CON the name suggests that RL would be a significant topic, while in reality it appeared only in the end of the course and important subjects such as TDlearning are missing.