Chevron Left
返回到 Deep Learning and Reinforcement Learning

学生对 IBM 技能网络 提供的 Deep Learning and Reinforcement Learning 的评价和反馈

4.6
108 个评分

课程概述

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....

热门审阅

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!

筛选依据:

1 - Deep Learning and Reinforcement Learning 的 19 个评论(共 19 个)

创建者 Gideon D

Apr 24, 2021

创建者 Rui T

Nov 3, 2021

创建者 Seif M M

Jan 12, 2021

创建者 Ashish P

Mar 29, 2021

创建者 R W

Jul 26, 2021

创建者 Bishal B

Apr 4, 2022

创建者 Yasar A

Apr 21, 2021

创建者 george s

Sep 7, 2021

创建者 Luis P S

Jun 21, 2021

创建者 Jose M

Feb 9, 2021

创建者 My B

Apr 30, 2021

创建者 Marwan K

Mar 30, 2022

创建者 Pavuluri V C

Sep 24, 2021

创建者 Volodymyr

Aug 22, 2021

创建者 Surbhi J

Dec 18, 2021

创建者 Neha M

Mar 29, 2021

创建者 Subhadip C

Jan 31, 2022

创建者 Bernard F

Mar 18, 2021

创建者 José A G P

May 18, 2022