Book contents
- A Hands-On Introduction to Data Science
- A Hands-On Introduction to Data Science
- Copyright page
- Contents
- Preface
- About the Author
- Part I Conceptual Introductions
- Part II Tools for Data Science
- Part III Machine Learning for Data Science
- 7 Machine Learning Introduction and Regression
- 8 Supervised Learning
- 9 Unsupervised Learning
- Part IV Applications, Evaluations, and Methods
- Book part
- Index
- References
9 - Unsupervised Learning
from Part III - Machine Learning for Data Science
Published online by Cambridge University Press: aN Invalid Date NaN
Book contents
- A Hands-On Introduction to Data Science
- A Hands-On Introduction to Data Science
- Copyright page
- Contents
- Preface
- About the Author
- Part I Conceptual Introductions
- Part II Tools for Data Science
- Part III Machine Learning for Data Science
- 7 Machine Learning Introduction and Regression
- 8 Supervised Learning
- 9 Unsupervised Learning
- Part IV Applications, Evaluations, and Methods
- Book part
- Index
- References
Summary
This chapter covers unsupervised learning, where algorithms analyze data without known true labels or outcomes. Unlike supervised learning, the goal is to discover hidden patterns and structures in data.
The chapter explores three main techniques: Agglomerative clustering works bottom-up, starting with individual data points and merging similar ones into larger clusters. Divisive clustering (including k-means) takes a top-down approach, splitting data into smaller groups. Both methods use distance matrices and dendrograms to visualize cluster relationships.
Expectation Maximization (EM) handles incomplete data by iteratively estimating missing parameters using maximum likelihood estimation. Model quality is assessed using AIC and BIC criteria.
The chapter also introduces reinforcement learning, where agents learn optimal actions through trial-and-error interactions with environments, receiving rewards or penalties. Applications include robotics, gaming, and autonomous systems. Throughout, the chapter emphasizes the creative, interpretive nature of unsupervised learning compared to more structured supervised approaches.
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- Information
- A Hands-On Introduction to Data Science with Python , pp. 270 - 300Publisher: Cambridge University PressPrint publication year: 2026
References
Further Reading and Resources
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