Introduction: Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical machine learning tasks are concept learning, function learning or “predictive modeling”, clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions, for example. Machine learning hopes that including the experience into its tasks will eventually improve the learning. The ultimate goal is to improve the learning in such a way that it becomes automatic, so that humans like ourselves don’t need to interfere anymore. This course is meant to introduce you to the basics of machine learning in R: more specifically, it will show you how to use R to work with the well-known machine learning.
Objectives: The course aims at providing an accessible introduction to various machine learning methods and applications in R. The core of the course focuses on unsupervised and supervised methods. The course helps the students to develop a project.
Eligibility: Need some knowledge about any programming language and need some concepts about statistics.
Duration – 80 hours
Daily / Weekly Classes
4 Case Studies & 1 Live Project
Professional Courses are only for the working professionals / experienced candidates