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What you would learn in Artificial Intelligence: Reinforcement Learning in Python course?
People speak of artificial intelligence; typically, they don't refer to unsupervised and supervised machine learning.
The tasks listed above are comparatively easy compared to what we imagine of AIs playing Go and chess or driving cars, as well as winning games at the level of a superhuman.
Reinforcement Learning is now gaining popularity for all of this and more.
Like the concept of deep learning, the majority of the theories were discovered around the time of the 70s and the 80s. Still, it's only been recent that we've had the chance to experience the astonishing results that could be possible.
In 2016, we witnessed Google's AlphaGo beat the world's champion in Go.
We saw AIs engaging in video games such as Doom or Super Mario.
Self-driving vehicles have begun to drive on actual roads, sharing the road with other drivers and carrying people ( Uber), All without human intervention.
If this sounds like a dream, prepare for the future, as the law of exponential returns means that this growth rate will only accelerate exponentially.
Learning about unsupervised and supervised machine learning is a huge task. So far, I've independently taken over Twenty-five (25!) courses on these topics.
Yet, reinforcement learning can open an entirely new world. As you'll discover in this course, The reinforcement learning model is quite different, unlike unsupervised and supervised learning.
This has led to the discovery of astonishing and innovative neuroscience and behavioral psychology insights. This course has many similar techniques to instructing an agent and teaching animals or humans. This is the closest thing we have to synthetic general intelligence. What's included in this course?
The multi-armed bandit dilemma and the exploration-exploit problem
Methods of calculating means and moving averages, as well as their relation to stochastic gradient descent
Markov Decision Processes (MDPs)
Temporal Variation (TD) Learn (Q-Learning in addition to SARSA)
Techniques for Approximation (i.e., how to integrate deep neural networks or any other model that is differentiable into the RL algorithm)
How do you utilize OpenAI Gym with zero modifications to the code
Project: Use Q-Learning to create an automated stock trading bot
Use gradient-based machine learning methods to reinforce learning
Recognize reinforcement learning on the technical level
Learn about the connection between reinforcement learning and psychological development
Apply 17 reinforcement-learning algorithms.
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