A quick Guide on Reinforcement Learning with Tensor flow 2.0
A course that will help you implement reinforcement learning in your projects!!
In the last few years, we heard about Google’s AlphaGo defeating the GO champion; we heard that the latest AIs are now playing Super Mario or Dota2, or even AI-powered self-driving cars (Tesla) have started carrying passengers without human assistance.
If all this sounds crazy, then brace yourself for the future because development in AI is increasing at a pace like never before. Reinforcement learning is one such development in AI that has opened a whole new world. To help you learn this concept, we are set to launch an entire curation dedicated to Reinforcement Learning.
Yes, this will be an exclusive curation that will help you in effective learning & implementing reinforcement learning in your projects. Also hailed as one of the smartest techniques of ML, reinforcement learning is very different from supervised & unsupervised learning. Though this upcoming course is created to take your AI skills to the next level.
This course will be suitable for:
- Data scientists looking to up their skills.
- Anyone who wants to implement reinforcement learning in their projects.
- Engineers involved with automation.
- Business leaders & organizations looking to gain an extra edge using reinforcement learning.
Why you should take this course?
It will be a complete course that will help you in learning reinforcement learning with a step-by-step approach. Its author, Irfan possesses extensive experience in Machine Learning, Deep Learning, Blockchain & Full Stack Development. Upon completing this course, you will be able to implement reinforcement-learning without any hassle.
This course will be primarily divided into 2 parts for a better understanding. The 1st part will help you in building a strong foundation, and the second part will help you learn the advanced concepts of reinforcement learning.
This course includes:
- Intro to reinforcement learning & its frameworks
- Reinforcement learning techniques & applications
- Reinforcement learning strategies
- Agent development
- MDP & dynamic programming
- Reinforcement learning algorithms
- Monte Carlo methods
Though this course will teach you everything from scratch but some basic knowledge of Python programming, algebra, probability are required. Additional knowledge of supervised & unsupervised learning will be a big plus.