Data Mining

Machine learning teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.

Request Access Explore All Courses

About the course

Show more Show less

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.

Skills you will gain

  • Unsupervised ML
  • Supervised ML
  • Decision Trees
  • Neural Networks

Course Syllabus

Module 1

Data Mining

9.5 Hrs

3 Quizzes
  • Terminologies of Machine Learning
  • Hierarchical Clustering
  • Clustering in R
  • K Means Clustering
  • Market Basket Analysis
  • Introduction to Classification Techniques
  • CART Model
  • Greedy Algorithm
  • Parsimony
  • Model Performance Measures
  • Random Forest
  • Introduction to Neural Networks
  • Concepts in Neural Networks
  • Neural Networks in R
  • Scaling of Variables
  • Show more


Identify potential customers for loans Product

Course certificate

Get Data Mining course completion certificate from Great learning which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.