KnowledgeCity

Analyzing Data Clusters

Introduction to clustering and modeling and their problem-solving uses.
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Course: On-Demand
Intermediate  Provider Briana Brownell  7 Lessons ·  36m  in Arabic, German, English, Spanish, French, Portuguese, Urdu, Chinese Simplified 

Course Description

These lessons will introduce clustering and its problem space within unsupervised machine learning. You’ll be introduced to the pairings of clustering with other modeling steps like dimension reduction, feature engineering, and prediction. You’ll also learn how to consider clustering from a business standpoint, along with what kinds of problems it’s useful for.

What You'll Learn

  • Recognize how clustering pairs with other areas of machine learning
  • Apply modeling steps including dimension reduction and prediction
  • Apply clustering to areas of business operations
  • Compare clustering techniques such as K-Means, hierarchical, and spectral clustering
  • Use similarity measures to analyze data clusters
  • Combine clustering with feature engineering

Key Takeaways

  • Clustering is part of the problem space within unsupervised machine learning.
  • Clustering can be paired with other modeling steps such as dimension reduction, feature engineering, and prediction.
  • Clustering can be considered from a business standpoint and applied to business operations.
  • The course covers clustering basics and similarity measures as foundations for cluster analysis.
  • Clustering is useful for certain kinds of problems, which the course helps identify.

Frequently Asked Questions

What does this course cover?

It introduces clustering and its problem space within unsupervised machine learning, including how clustering pairs with other modeling steps like dimension reduction, feature engineering, and prediction, and how to consider clustering from a business standpoint.

What skills will I gain from this course?

You will build skills in cluster analysis, data classification, hierarchical clustering, K-Means clustering, spectral clustering, and unsupervised learning.

What topics are taught in the lessons?

Lessons cover Unsupervised Machine Learning, Clustering Basics, Similarity Measures, Clustering and Dimension Reduction, Clustering and Feature Engineering, Clustering and Prediction, and Using Data Clusters.

How does this course connect clustering to business?

It teaches how to consider clustering from a business standpoint, what kinds of problems it is useful for, and how to apply clustering to areas of business operations.