Introduction

Machine learning algorithms (MLAs) facilitates the organization to find deeper insights and drive real-time actions. At the heart of business analytics lays the MLA, which automates decision making by building predictive models. The analytics world requires data scientist to understand and implement MLA for data driven decisions. Artificial Intelligence and neural networks are the next step forward in terms of automating repetitive decisions, expert decisions in some cases. Hence it is imperative for the faculty members to have knowledge of MLA and Artificial intelligence and practically understand how to unravel the hidden insights from the humongous amount of data that society in general and organization in particular have produced. The knowledge gained would enable the faculty to stream line their analytics curriculum and build the requisite business analytics skillset in their students and thereby making them industry ready.

Objectives

This certificate program of 10 weeks would lead the participants to understand and assimilate the widely applied Machine Learning Algorithms(MLAs) in the industry.
It also would walk the participant through future scope of MLAs for various industry applications including but not limited to deep learning and artificial neural networks.
This would be done through balanced mix of conceptual and practical sessions using real world datasets. The Hands On sessions would be held using Python, R and Tableau software.

Expected Outcomes

According to the Harvard Business Journal and Fortune magazine a career as a data scientist is “the” job to have in the 21st century. At the same time, the McKinsey Global Institute’s Big Data Report notes that by 2018, the U.S. alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of “Big Data” to make effective decisions.

This Certificate program is designed with the following outcomes in mind:

Develop clear understanding of the concepts and applications of widely used MLAs by industry
Understand how to apply the various MLAs techniques using R-language (analytics software) on the real world datasets to make data driven decisions
To appreciate and understand the future scope of MLAs in various business domains. Viz., deep learning and artificial neural networks.