Practical AI with Python and Reinforcement Learning
What you'll learn
- 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
- 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!
Please note! This course is in an "early bird" release, and we're still updating and adding content to it, please keep in mind before enrolling that the course is not yet complete.
“The future is already here – it’s just not very evenly distributed.“
Have you ever wondered how Artificial Intelligence actually works? Do you want to be able to harness the power of neural networks and reinforcement learning to create intelligent agents that can solve tasks with human level complexity?
This is the ultimate course online for learning how to use Python to harness the power of Neural Networks to create Artificially Intelligent agents!
This course focuses on a practical approach that puts you in the driver's seat to actually build and create intelligent agents, instead of just showing you small toy examples like many other online courses. Here we focus on giving you the power to apply artificial intelligence to your own problems, environments, and situations, not just those included in a niche library!
This course covers the following topics:
Artificial Neural Networks
Convolution Neural Networks
Cross Entropy Methods
and much more!
We've designed this course to get you to be able to create your own deep reinforcement learning agents on your own environments. It focuses on a practical approach with the right balance of theory and intuition with useable code. The course uses clear examples in slides to connect mathematical equations to practical code implementation, before showing how to manually implement the equations that conduct reinforcement learning.
We'll first show you how Deep Learning with Keras and TensorFlow works, before diving into Reinforcement Learning concepts, such as Q-Learning. Then we can combine these ideas to walk you through Deep Reinforcement Learning agents, such as Deep Q-Networks!
There is still a lot more to come, I hope you'll join 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
Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings.