When faced with the task of extracting information from an as yet unseen large collection of documents, topic modeling is a great approach, as it provides insights into the underlying structure of the documents. That is, topic models find word groupings using proximity, not context. In this chapter, we have learned how to apply two of the most common and most effective topic modeling algorithms: latent Dirichlet allocation and non-negative matrix factorization. We should now feel comfortable cleaning raw text documents using several different techniques; techniques that can be utilized in many other modeling scenarios. We continued by learning how to convert the cleaned corpus into the appropriate data structure of per-document raw word counts or word weights by applying bag-of-words models. The main focus of the chapter was fitting the two topic models, including optimizing the number of topics, converting the output to easy-to-interpret tables, and visualizing the results. With...

#### 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.

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