Chevron Left
返回到 Deep Learning and Reinforcement Learning

學生對 IBM 技能网络 提供的 Deep Learning and Reinforcement Learning 的評價和反饋

4.6
118 個評分

課程概述

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....

熱門審閱

YE

2021年4月20日

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

2021年2月8日

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

篩選依據:

1 - Deep Learning and Reinforcement Learning 的 19 個評論(共 19 個)

創建者 Gideon D

2021年4月24日

創建者 Rui T

2021年11月3日

創建者 Seif M M

2021年1月12日

創建者 Ashish P

2021年3月29日

創建者 R W

2021年7月26日

創建者 Bishal B

2022年4月4日

創建者 Yasar A

2021年4月21日

創建者 george s

2021年9月7日

創建者 Luis P S

2021年6月21日

創建者 Jose M

2021年2月9日

創建者 My B

2021年4月30日

創建者 Marwan K

2022年3月30日

創建者 Pavuluri V C

2021年9月24日

創建者 Volodymyr

2021年8月22日

創建者 Surbhi J

2021年12月18日

創建者 Neha M

2021年3月29日

創建者 Subhadip C

2022年1月31日

創建者 Bernard F

2021年3月18日

創建者 José A G P

2022年5月18日