Unsupervised learning is a useful and practical solution in situations where labeled data

is not available.

Applied Unsupervised Learning with Python guides you through the best practices for

using unsupervised learning techniques in tandem with Python libraries to extract

meaningful information from unstructured data. The book begins by explaining how

basic clustering works to find similar data points in a dataset. Once you are well-versed

with the k-means algorithm and how it operates, you'll learn what dimensionality

reduction is and where to apply it. As you progress, you'll learn various neural network

techniques and how they can improve your model. While studying the applications

of unsupervised learning, you will also learn how to mine topics that are trending on

Twitter. You will complete the book by challenging yourself with various interesting

activities, such as performing market basket analysis and identifying relationships

between different products.

By the end of this book, you will have the skills you need to confidently build your own

models using Python.

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First Published: May 2019