In this chapter, we covered the process of dimensionality reduction and PCA. We completed a number of exercises and developed the skills to reduce the size of a dataset by extracting only the most important components of variance within the data, using both a manual PCA process and the model provided by scikit-learn. During this chapter, we also returned the reduced datasets back to the original dataspace and observed the effect of removing the variance on the original data. Finally, we discussed a number of potential applications for PCA and other dimensionality reduction processes. In our next chapter, we will introduce neural network-based autoencoders and use the Keras package to implement them.
Applied Unsupervised Learning with Python
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Applied Unsupervised Learning with Python
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Overview of this book
Unsupervised learning is a useful and practical solution in situations where labeled data is not available.
Applied Unsupervised Learning with Python guides you in learning the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The book begins by explaining how basic clustering works to find similar data points in a set. 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 understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. Finally, you will be able to put your knowledge to work through interesting activities such as performing a 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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Current Title:
Applied Unsupervised Learning with Python
Table of Contents (12 chapters)
Applied Unsupervised Learning with Python
Preface
Free Chapter
Introduction to Clustering
Hierarchical Clustering
Neighborhood Approaches and DBSCAN
Dimension Reduction and PCA
Autoencoders
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Topic Modeling
Market Basket Analysis
Hotspot Analysis
Appendix
Customer Reviews