Machine Learning Specialist - Deep Learning and Reinforcement Learning [W7141G]
Beschreibung
Ontdek de verschillende trainingsmogelijkheden bij Global Knowledge
Online of op locatie er is altijd een vorm die bij je past.
Kies op welke manier jij of je team graag een training wilt volgen. Global Knowledge bied je verschillende trainingsmogelijkheden. Je kunt kiezen uit o.a. klassikaal, Virtueel Klassikaal (online), e-Learning en maatwerk. Met onze Blended oplossing kun je de verschillende trainingsvormen combineren.
OVERVIEW
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 Rei…
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Ontdek de verschillende trainingsmogelijkheden bij Global Knowledge
Online of op locatie er is altijd een vorm die bij je past.
Kies op welke manier jij of je team graag een training wilt volgen. Global Knowledge bied je verschillende trainingsmogelijkheden. Je kunt kiezen uit o.a. klassikaal, Virtueel Klassikaal (online), e-Learning en maatwerk. Met onze Blended oplossing kun je de verschillende trainingsvormen combineren.
OVERVIEW
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.
IBM Customers and Sellers: If you are interested in this course, consider purchasing it as part of one of these Individual or Enterprise Subscriptions:
- IBM Learning for Data and AI Individual Subscription (SUBR022G)
- IBM Learning for Data and AI Enterprise Subscription (SUBR004G)
- IBM Learning Individual Subscription with Red Hat Learning Services (SUBR023G)
OBJECTIVES
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
CONTENT
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