In this episode of our machine learning mini-series, we explore the world of Reinforcement Learning (RL). Think of RL as the rebellious teenager of the machine learning family, eager to learn through trial and error. We’ll break down the basics: from agents and environments to actions, rewards, and policies. Using engaging analogies like training a dog or a game show contestant, we’ll explore real-world applications, including self-driving cars, video games, robotics, and marketing. Plus, we'll discuss the challenges of balancing exploration with exploitation and the hefty data requirements that make RL both fascinating and formidable.
 
Connect with Emily Laird on LinkedIn

Ver todos los episodios