Machine Learning, categorically falls under predictive analytics and is fundamentally associated with information discovery in databases. Machine Learning aims at finding useful patterns from large data sets in an attempt to make data more informative and qualitatively insightful. The value of patterns discovered from mining the data enables businesses to make effective data-driven decisions and develop sustainable competitive advantage. Applications of data mining can be found in e-commerce, social welfare, politics, terrorism, sales and marketing, finance, operations etc.
In this course we explore how this field brings together techniques from statistics, machine learning, and information retrieval. We will discuss the main data mining methods currently used, including clustering, classification; association rules mining, neural networks, decision trees and random forest.