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Deep Learning and Reinforcement Learning

Course content updated by LearnQuest
Price
425 USD
14 Hours
W7105GS
Self-Paced Training
IBM Business Partner
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Course Description

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

Audience

This course targets aspiring data scientists interested in acquiring hands-on experience with Deep Learning and Reinforcement Learning.

Prerequisites

    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.

Topics

1. Introduction to Neural Networks

2. Neural Network Optimizers and Keras

3. Convolutional Neural Networks

4. Recurrent Neural Networks and Long-Short Term Memory Networks

5. Deep Learning with Autoencoders

6. Deep Learning Applications and Reinforcement Learning

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Self-Paced Training Info

Learn at your own pace with anytime, anywhere training

  • Same in-demand topics as instructor-led public and private classes.
  • Standalone learning or supplemental reinforcement.
  • e-Learning content varies by course and technology.
  • View the Self-Paced version of this outline and what is included in the SPVC course.
  • Learn more about e-Learning

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Self-Paced Training Terms & Conditions

THIS IS A SELF-PACED VIRTUAL CLASS. AFTER YOU REGISTER, YOU HAVE 30 DAYS TO COMPLETE THE COURSE.

This is a Self-Paced virtual class; it is intended for students who do not need the support of a classroom instructor. If you feel you would better benefit from having access to a Subject Matter Expert, please enroll in the Instructor-Led version instead. Minimal technical support is provided to address issues with accessing the platform or problems within the lab environment.

Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form. Self-Paced Virtual Classes are non-refundable. Once you purchase a Self-Paced Virtual Class, you will be charged the full price.

After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. A confirmation email will contain your online link, your ID and password, and additional instructions for starting the course.

You can start the course at any time within 12 months of enrolling for the course. After you register/start the course, you have 30 days to complete your course. Within this 30 days, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.

If the course requires a remote lab system, the lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system.

Click the Skytap Connectivity Test button to ensure this computer's hardware, software and internet connection works with the SPVC Lab Environment.

Click the Skytap Connectivity Documentation button to read about the hardware, software and internet connection requirements.

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