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雷达卡
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.
pages 483
First Published: May 2019
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