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When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning.These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level.Reinforcement learning has recently become popular for doing all of that and more.Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible.In 2016 we saw Google's AlphaGo beat the world Champion in Go.We saw AIs playing video games like Doom and Super Mario.Self-driving cars have started driving on real roads with other drivers and even carrying passengers (Uber), all without human assistance.If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially.Learning about supervised and unsupervised machine learning is no small feat.And yet reinforcement learning opens up a whole new world. As you'll learn in this course, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other.It's led to new and amazing insights both in behavioural psychology and neuroscience. As you'll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It's the closest thing we have so far to a true general artificial intelligence. What's covered in this course?Deep Learning.Google ColabAnaconda.Jupiter Notebook.Activation Function.Keras.Pandas.TensorFlow 2.0Neural NetworkMatplotlib.scikit-learn.OpenAI Gym.Pytorch.Policy gradient algorithm.Markov Chain.Policy iteration algorithm.Monte Carlo method.Q-Learning.Deep-Q networks.Double Deep-Q networks.Duelling Deep-Q networks.REINFORCE algorithm.The multi-armed bandit problem.Ways to calculate means and moving averages and their relationship to stochastic gradient descent.Markov Decision Processes (MDPs).Dynamic Programming.Temporal Difference (TD) Learning (Q-Learning and SARSA).Actor-critic algorithm.Advantage Actor-Critic (A2C).Deep Recurrent Q-Learning algorithm and DRQN agent Implementation.Asynchronous Advantage Actor-Critic algorithm and A3C agent Implementation.Proximal Policy Optimization algorithm and PPO agent Implementation.Deep Deterministic Policy Gradient algorithm and DDPG agent Implementation.Contextual bandits.If you're ready to take on a brand new challenge, and learn about AI techniques that you've never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you.Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are five big projects on healthcare problems and one small project to practice. These projects are listed below:Robot control.Hill Climbing game.Atari game.Frozen Lake environment.Coin Flipping gambleCalculating Pi.Blackjack game.Windy Gridworld environment playground.Taxi problem.The MAB problem.Mountain car environment.Online Advertisement.Cryptocurrency Trading Agents.Building Stock/Share Trading Agents.That is all. See you in class!"If you can't implement it, you don't understand it"Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".My courses are the ONLY course where you will learn how to implement deep REINFORCEMENT LEARNING algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

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