O.P. Jindal Global University
Unsupervised Learning and Its Applications in Marketing
O.P. Jindal Global University

Unsupervised Learning and Its Applications in Marketing

This course is part of Machine Learning for Marketing Specialization

Taught in English

Ambica Ghai

Instructor: Ambica Ghai

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Beginner level

Recommended experience

21 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Apply Python as an effective tool for implementing various algorithms.

  • Describe unsupervised learning and list its various algorithms.

  • List the various applications and promising areas for the application of unsupervised learning.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

36 quizzes

Course

Gain insight into a topic and learn the fundamentals

Beginner level

Recommended experience

21 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

Placeholder

Build your subject-matter expertise

This course is part of the Machine Learning for Marketing Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

There are 12 modules in this course

In this module, you will be introduced to the exciting field of unsupervised learning and its applications in marketing. You will learn about various unsupervised learning algorithms and their functionalities, including clustering, dimensionality reduction, and association rule mining. Through hands-on exercises and practical examples, you will understand how these techniques can be used to uncover hidden patterns, identify customer segments, and gain valuable insights from large and complex marketing datasets. By the end of this module, you will have the knowledge and skills to apply unsupervised learning algorithms to solve marketing challenges, optimize campaigns, and make data-driven decisions that drive business growth. Get ready to unlock the potential of unsupervised learning and revolutionize your marketing strategies.

What's included

5 videos5 readings4 quizzes1 discussion prompt

This module provides a comprehensive introduction to clustering algorithms and their practical application using Python. You will gain a solid understanding of the fundamental concepts of clustering and explore different algorithms such as k-means, hierarchical clustering, and DBSCAN. Through hands-on exercises and coding examples, you will learn how to preprocess and transform data, select appropriate clustering algorithms based on data characteristics, and evaluate the performance of clustering models. Additionally, you will acquire the necessary skills to interpret and visualize clustering results, allowing you to gain valuable insights into patterns and structures within your data. By the end of this module, you will be equipped with the knowledge and practical experience to confidently apply clustering algorithms to real-world marketing datasets, enabling you to uncover meaningful clusters and make informed business decisions based on the extracted knowledge.

What's included

4 videos4 readings4 quizzes

This assessment is a graded quiz based on the modules covered this week.

What's included

1 quiz

In this module, you will dive into the fascinating world of customer segmentation and dimensionality reduction techniques. Customer segmentation allows you to divide your customer base into distinct groups based on shared characteristics, behaviors, or preferences. By understanding the unique needs and preferences of different customer segments, you can tailor your marketing strategies to effectively target and engage each segment. You will learn various clustering algorithms and techniques to perform customer segmentation using Python, enabling you to uncover meaningful insights about your customers and optimize your marketing efforts. Additionally, you will explore dimensionality reduction techniques, which are essential for dealing with high-dimensional data and extracting the most relevant features. Through hands-on exercises and real-world examples, you will gain practical skills in implementing customer segmentation and dimensionality reduction techniques to unlock valuable insights and drive marketing success.

What's included

4 videos4 readings4 quizzes1 discussion prompt

This module provides an opportunity to apply dimensionality reduction algorithms using Python. You will explore different types of dimensionality reduction algorithms, such as Principal Component Analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and autoencoders. Through practical exercises and code implementations, you will gain hands-on experience in reducing the dimensionality of datasets, visualizing high-dimensional data in lower dimensions, and interpreting the results. Additionally, you will be introduced to anomaly detection techniques, which involve identifying rare or unusual data points that deviate from the norm. By the end of this module, you will have a solid understanding of dimensionality reduction algorithms and their application in real-world marketing scenarios, as well as the ability to detect anomalies effectively.

What's included

4 videos4 readings4 quizzes

This assessment is a graded quiz based on the modules covered this week.

What's included

1 quiz

In this module, you will delve into the practical aspects of anomaly detection by implementing various types of anomaly detection algorithms using Python. You will gain hands-on experience in applying algorithms such as statistical methods, clustering-based approaches, and machine learning-based techniques to detect anomalies in marketing data. Through step-by-step coding examples and guided exercises, you will learn how to preprocess data, select appropriate algorithms for different scenarios, tune parameters, and evaluate the performance of the models. By the end of this module, you will have a solid understanding of the implementation details of different anomaly detection algorithms and be equipped to apply them effectively in real-world marketing scenarios.

What's included

4 videos4 readings4 quizzes

Welcome to the module on Autoencoders and Association Learning! In this module, you will explore the fascinating field of autoencoders and its application in association learning, specifically in market basket analysis. In this module, you will learn how to apply autoencoders to extract meaningful features from data and use them to perform association learning using techniques such as the Apriori algorithm and FP-Growth algorithm. Through hands-on exercises and real-world examples, you will gain practical skills in implementing autoencoders and conducting association analysis to discover valuable insights from large-scale transactional data.

What's included

4 videos4 readings4 quizzes1 discussion prompt

This assessment is a graded quiz based on the modules covered this week.

What's included

1 quiz

In this module, you will delve into the world of semi-supervised learning. Semi-supervised learning is a powerful technique that combines the strengths of both supervised and unsupervised learning. It leverages a small amount of labeled data along with a large amount of unlabeled data to improve model performance. Through this module, you will gain an understanding of the concepts and principles behind semi-supervised learning. You will also learn how to implement semi-supervised learning algorithms using Python, enabling you to leverage the vast amounts of unlabeled data available in many real-world scenarios. By the end of this module, you will have the knowledge and skills to apply semi-supervised learning techniques in various domains, unlocking new opportunities for predictive modeling and data analysis.

What's included

4 videos4 readings4 quizzes1 discussion prompt

In this module, you will delve into the fascinating world of recommender systems and explore the concept of Boltzmann machines, which are powerful generative unsupervised models. You will gain a solid understanding of how Boltzmann machines work and their applications in recommendation systems. Through hands-on exercises and practical examples in Python, you will learn how to implement collaborative filtering using Boltzmann machines to make personalized recommendations. Additionally, this module will also touch upon the promising areas of unsupervised learning and provide insights into the future possibilities and advancements in the field. By the end of this module, you will be equipped with the knowledge and skills to build effective recommender systems and have a broader understanding of the potential of unsupervised learning in various domains.

What's included

4 videos4 readings4 quizzes

This assessment is a graded quiz based on the modules covered this week.

What's included

1 video1 quiz

Instructor

Ambica Ghai
O.P. Jindal Global University
2 Courses876 learners

Offered by

Recommended if you're interested in Marketing

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

New to Marketing? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions