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Generative Adversarial Networks and Reinforcement Learning

IntermediateCourse

Generative adversarial networks (GANs) and Reinforcement Learning (RL) are highly sought-after skills in the job market. Companies across various industries are looking for professionals who can harness the potential of these technologies to drive innovation and solve complex problems. Discover why GANs are hailed as a game-changers, offering the ability to generate life-like content and revolutionize fields like art, medicine, and more. Learn why RL is the cornerstone of AI breakthroughs, from autonomous driving to game-playing AI.

4.5 (183 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 678

Skills You Will Learn

  • Artificial Intelligence, Data Science

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 3 hours

Platform

  • SkillsNetwork

Last Update

  • May 7, 2025
About this Course
By mastering GANs and RL, you'll become a valuable asset in today's AI-driven world. Learn about Generative Adversarial Networks, enabling the creation of realistic artificial data and images and explore Reinforcement Learning to teach AI agents how to learn and adapt in diverse environments. Whether you're an artist, designer, or content creator, you can use GANs to generate unique artwork, music, and more. Imagine creating entirely new art styles or generating stunning visuals for your projects – the possibilities are endless. Using Rl, you can build AI agents that learn from their environment, enabling them to excel in complex tasks like mastering games, optimizing resource allocation, and even providing personalized recommendations.

 IBM has a special offer for watsonx.ai a studio for new foundation models, generative AI and machine learning. To take advantage of this offer visit watsonx.ai homepage. 

Enroll and don't miss the chance to be at the forefront of AI innovation.

Recommended Skills Prior to Taking this Course

A good understanding of Keras, Linear Regression and Classification and Neural Networks Principles in addition to Python programming language.  

Instructors

Artem Arutyunov

Data Scientist

Hey, Artem here! I am excited about answering new challenges with data science, machine learning and especially Reinforcement Learning. Love helping people to learn, and learn myself. Studying Math and Stats at University of Toronto, hit me up if you are from there as well.

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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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Contributors

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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