Unsupervised Machine Learning

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.

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About the course

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Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning,which finds hidden patterns or intrinsic structures in input data.

In this course you will understand how Unsupervised Learning contrasts with Supervised Learning and apply commonly used unsupervised learning techniques including clustering and principal component analysis.

Skills you will gain

  • K means
  • Hierarchical clustering
  • PCA

Course Syllabus

Module 1

Unsupervised Machine Learning

6.5 Hrs

1 Quiz
  • Types of clustering and clustering distance
  • Euclidean and Non Euclidean distance
  • K-means clustering
  • Elbow method
  • Visual analysis and Dynamic Clustering
  • Hands on exercise on K-means clustering
  • Hierarchial Clustering
  • Measuring distance between clusters and Complete linkage
  • Hands on exercise on Hierarchical clustering
  • Principal Component Analysis concepts
  • Principal Component Covariance Matrix
  • PCA for Dimensionality Reduction
  • Hands on exercise on PCA
  • Introduction to Feature engineering
  • Hands on exercise for feature engineering
  • Feature engineering lab exercise using different algorithms
  • How to tune the models or improve performance
  • Concept of upsampling and downsampling
  • Hands on exercise showing tuning of model
  • Show more


Clustering cars based on attributes

Classifying silhouettes of vehicles

Course certificate

Get Unsupervised Machine Learning 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.