Practical AI with Python and Reinforcement Learning

Practical AI with Python and Reinforcement Learning paid course free. You will Learn how to use Reinforcement Learning techniques to create practical Artificial Intelligence programs in this complete course.

  • Reinforcement Learning with Python
  • Creating Artificial Neural Networks with TensorFlow
  • Using TensorFlow to create Convolution Neural Networks for Images
  • Using OpenAI to work with built-in game environments
  • Using OpenAI to create your own environments for any problem
  • Create Artificially Intelligent Agents
  • Tabular Q-Learning
  • State–action–reward–state–action (SARSA)
  • Deep Q-Learning (DQN)
  • DQN using Convolutional Neural Networks
  • Cross Entropy Method for Reinforcement Learning
  • Double DQN
  • Dueling DQN

Practical AI with Python and Reinforcement Learning Course Requirements

  • You should be very comfortable with basic Python and installing Python libraries.
  • This is NOT a course for beginners, we highly suggest you take our “Data Science and Machine Learning Masterclass” first!

Practical AI with Python and Reinforcement Learning Course Description

If it’s not too much trouble, note! This course is in an “morning person” delivery, we’re actually refreshing and adding content to it, if it’s not too much trouble, remember prior to selecting that the course isn’t yet finished.

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“What’s to come is as of now here – it’s simply not equitably conveyed.”

Have you at any point considered how Artificial Intelligence really functions? Would you like to have the option to outfit the force of neural organizations and support figuring out how to make clever specialists that can address assignments with human level intricacy?

This is a definitive course online for figuring out how to utilize Python to outfit the force of Neural Networks to make Artificially Intelligent specialists!

This course centers around a reasonable methodology that places you steering the ship to really fabricate and make savvy specialists, rather than simply showing you little toy models like numerous other online courses. Here we center around enabling you to apply computerized reasoning to your own issues, surroundings, and circumstances, not simply those remembered for a specialty library!

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This course covers the accompanying points:

Counterfeit Neural Networks

Convolution Neural Networks

Old style Q-Learning

Profound Q-Learning


Cross Entropy Methods

Twofold DQN

what’s more, considerably more!

We’ve planned this course to persuade you to have the option to make your own profound support learning specialists on your own surroundings. It’s anything but a commonsense methodology with the right equilibrium of hypothesis and instinct with useable code. The course utilizes clear models in slides to associate numerical conditions to commonsense code execution, prior to telling the best way to physically carry out the conditions that lead support learning.

We’ll initially show you how Deep Learning with Keras and TensorFlow works, prior to jumping into Reinforcement Learning ideas, like Q-Learning. Then, at that point we can consolidate these plans to walk you through Deep Reinforcement Learning specialists, like Deep Q-Networks!

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There is still significantly more to come, I trust you’ll go along with us inside the course!

Who this course is for:

  • Python developers familiar with basics of machine learning, such as Scikit-Learn, but now want to learn how to create Artificially Intelligent Agents through Reinforcement Learning

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Practical AI with Python and Reinforcement Learning

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