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Deep Learning and Reinforcement Learning

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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.

4.6 (253 Reviews)

Language

  • English

Topic

  • Machine Learning

Enrollment Count

  • 37.79K

Skills You Will Learn

  • Machine Learning, Deep Learning, Artificial Neural Networks, Computer Vision, Convolutional Neural Network

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 32 hours

Platform

  • Coursera

Last Update

  • June 5, 2025
About this Course
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.

Instructors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Roxanne Li

Data Scientist at IBM

I am an aspiring Data Scientist at IBM with extensive theoretical/academic, research, and work experience in different areas of Machine Learning, including Classification, Clustering, Computer Vision, NLP, and Generative AI. I've exploited Machine Learning to build data products for the P&C insurance industry in the past. I also recently became an instructor of the Unsupervised Machine Learning course by IBM on Coursera!

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Kopal Garg

Data Scientist Intern at IBM

I am a Data Scientist Intern at IBM, and a Masters student in Computer Science at the University of Toronto. I am passionate about building data science, and machine learning-based systems for improving various aspects of life.

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Contributors

Sam Prokopchuk

Software Engineer, Data Scientist Intern

Software Engineer/Data Scientist Intern at IBM.

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Richard Ye

Skills Network Data Scientist

A student of statistics interested in Machine Learning, Deep Learning (NLP specifically) and software development.

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Cindy Huang

Data Science Intern at IBM

Hey there! I'm a senior at the University of Toronto studying data science. My passion for machine learning lies in NLP and using technology to improve human experience.

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