Chevron Left
Voltar para Deep Learning and Reinforcement Learning

Comentários e feedback de alunos de Deep Learning and Reinforcement Learning da instituição IBM Skills Network

118 classificações

Sobre o curso

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....

Melhores avaliações


20 de abr de 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.


8 de fev de 2021

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

Filtrar por:

1 — 19 de 19 Avaliações para o Deep Learning and Reinforcement Learning

por Gideon D

24 de abr de 2021

por Rui T

3 de nov de 2021

por Seif M M

12 de jan de 2021

por Ashish P

29 de mar de 2021

por R W

26 de jul de 2021

por Bishal B

4 de abr de 2022

por Yasar A

21 de abr de 2021

por george s

7 de set de 2021

por Luis P S

21 de jun de 2021

por Jose M

9 de fev de 2021

por My B

30 de abr de 2021

por Marwan K

30 de mar de 2022

por Pavuluri V C

24 de set de 2021

por Volodymyr

22 de ago de 2021

por Surbhi J

18 de dez de 2021

por Neha M

29 de mar de 2021

por Subhadip C

31 de jan de 2022

por Bernard F

18 de mar de 2021

por José A G P

18 de mai de 2022