Unsupervised Machine Learning

This learning track introduces the fundamental concepts of dimensionality reduction through the PCA technique along with its Python implementation and explores the theory and implementation of various Unsupervised learning algorithms  like Hierarchical Clustering, Non-Hierarchical Clustering, K-Means Clustering along with Cluster analysis and evaluation.

  • icons final-02 4 Courses
  • icons final-03 5 Projects & Case Studies
Unsupervised Machine Learning
  • 2 bar graph
    Difficulty: Intermediate

    Foundational knowledge or experience in machine learning is recommended

  • Asset 1
    Duration: Approximately 5 Weeks

    Suggested learning pace is 5hr/week

Course Overview

  • Learn the basic principles of dimensionality reduction techniques and their importance, along with the working and implementation of the PCA technique to deal with the high dimensionality of a dataset.
  • Understand the basic idea of Unsupervised Learning and the theoretical concepts behind various clustering algorithms.
  • Learn how to implement the various clustering algorithms in Python and perform detailed cluster analysis through visualizations.
  • Learn how to apply the concepts learned on live data across industries to generate insights.

What’s included


Shareable Certificate

Earn a sharable certificate upon completion


Lifetime Access

Access this learning track for life once completed


Flexible Scheduling

Start learning online immediately, at your own pace


Desktop Only

We recommend completing this learning track on a desktop

Skills You Will Learn


Objective Segmentation

Subjective Segmentation

Hierarchical Clustering

Non-Hierarchical Clustering

K-Means Clustering

Cluster Visualization

Cluster Evaluation

Cluster Analysis


  • Concepts and Application of Objective and Subjective Segmentation
  • Understanding Principal Component Analysis (PCA)
  • Clustering algorithms in Python
  • PCA in Python

  • Customer Segmentation in Retail – Application of K-Means Clustering Algorithm
  • Customer Segmentation in Insurance- Application of K-Means Clustering Algorithm
  • Application of Non-Hierarchical Clustering in HR Analytics Domain
  • Application of various clustering techniques to group the steel type based on its mechanical properties
  • Customer Segmentation Based on the Transaction History using Advance Clustering Techniques

How it Works

Learn new skills that will boost your career by enrolling in courses across data analytics, data science, ML and AI. These courses will utilize readings, videos, quizzes, data cases, and even coding exercises to teach you skills and concepts in a way that will solidify your new knowledge for hands-on application.

With our hands-on projects, you will take your newly learned skills along with our 750+ low-code/no-code functions and embedded coding console to complete milestone-based projects. Once completed, you will have effectively applied new skills and concepts to real-world data cases that can be translated directly into your career.

Complete assessments and track your progress in real-time to benchmark your proficiency in relation to key functional areas. As you progress through your courses, our patented platform will utilize ML and AI to record and analyze your inputs and output to provide active feedback and recommendations that will help you learn more effectively than the standard Letter Grade system used today.

Learner Outcomes

Complete learning tracks to earn sharable certificates and badges. These awarded items will be look great in your portfolio, resume, and LinkedIn as you showcase your skills and project experience to employers and colleagues.

  • Develop a working understanding of PCA, a dimensionality reduction technique, using Python.
  • Strong theoretical understanding of various clustering algorithms like Hierarchical, Non-Hierarchical and K-Means clustering.
  • Ability to implement different clustering algorithms to fulfil various objectives like segmentation, followed by a detailed analysis of the cluster results through visualization and relevant metrics.
  • Interpret and visualize the outcome of the various applied techniques.

“Rolai provides contextual upskilling opportunities … on one single platform.”

Sundar Ramamoorthy
Managing Director of Solutions.AI, Global Products & Delivery Lead at Accenture

“An excellent tool for anyone who wants to quickly learn the ropes.”

Sanket Kawde
Head Data and Analytics at CitiBank India

“Rolai is the best program available for someone looking to enhance their skills”

Connor McEachron
Planning & Analytics @ Brooks Brothers

“Great way to learn data analytics and data science”

Balaji Reddy
Manager – Applications Development

“The courses were excellent and covered topics that I didn’t expect”

Aadarsha G
Student At Ohio Wesleyan University
All the Most Frequently Asked Questions

What People Are Asking About Data Education

Rolai’s patented process provides a personalized learning process for each user. Rolai goes deeper than simply learning concepts and testing your skills. At Rolai, learners can apply their skills to actual industry use cases and projects.

Our courses include readings, videos, quizzes, and hands-on data cases that are completed using our virtual lab; give learners an applied learning experience.

No additional tools are needed to begin learning with Rolai. Our virtual lab contains the necessary data workspace and an embedded coding console.

  • We have internal SMEs across industries and domains that we work with to develop relevant content and assure quality datasets and problem statements.
  • We also work with enterprises and universities to develop new content directed towards their industry and expertise.