Machine Learning - Reinforcement Learning
  • 27-Nov-2021 ,

Webinar Type: ETT

Technology: Machine Learning Models

Practice: DATA & BI

Duration: 2 Hrs.

Speaker: Ashwini

RegistrationClick here

With supervised and unsupervised learning, the AI models have reached an acceptable accuracy level in predictive and prescriptive analytics. The companies will now move towards reinforcement learning using experience-driven sequential decision-making. 
In reinforcement learning, AI programming is set up with various conditions that characterize what sort of activity will be performed by the software. In light of different actions and results, the software self-learns actions to perform to meet the ideal ultimate objective.
This method interacts with the environment to learn and drives decisions towards a goal that rewards the actions taken. Without the need to specify all the asset failure scenarios, the algorithm learns repeatedly by exploring all possible options.


  • What are ML models?
  • Supervised and Unsupervised ML
  • Introducing Reinforcement Learning
  • Uses cases of Reinforcement Learning

Understand self learning models using Reinforcement Learning and the uses cases of the same