UNIT 14 REINFORCEMENT LEARNING (RL) ALGORITHM IN MACHINE LEARNING
This module introduces Reinforcement Learning algorithms, their basic framework, key components, core pillars, and workflow. It covers the mathematical foundations of Reinforcement Learning, exploration versus exploitation, evolution of RL capabilities, advantages, limitations, industrial applications, and comparison with other Machine Learning paradigms.
Module Overview
This module introduces Reinforcement Learning algorithms, their basic framework, key components, core pillars, and workflow. It covers the mathematical foundations of Reinforcement Learning, exploration versus exploitation, evolution of RL capabilities, advantages, limitations, industrial applications, and comparison with other Machine Learning paradigms.
What you will learn here
This course introduces the fundamentals of Machine Learning, including data types, data preprocessing, regression, classification, clustering, ensemble learning, Support Vector Machines, Decision Trees, K-Nearest Neighbours, Naïve Bayes, Random Forest, and Reinforcement Learning. It equips learners with practical skills to prepare data, build and evaluate Machine Learning models, implement algorithms using Python, and develop effective real-world predictive solutions.
How to access
Learn at your ownStart and pause anytime.
Accessible on all devicesLearn on mobile, tablet or desktop.
