Book Image

Machine Learning Quick Reference

By : Rahul Kumar
Book Image

Machine Learning Quick Reference

By: Rahul Kumar

Overview of this book

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Topic modeling 


Modeling is a methodology that's used to identify a topic and derive hidden patterns exhibited by a text corpus.Topic modeling resembles clustering, as we provide the number of topics as a hyperparameter (similar to the one used in clustering), which happens to be the number of clusters (k-means). Through this, we try to extract the number of topics or texts having some weights assigned to them.

The application of modeling lies in the area of document clustering, dimensionality reduction, information retrieval, and feature selection.

There are multiple ways to perform this, as follows:

  • Latent dirichlet allocation (LDA): It's based on probabilistic graphical models
  • Latent semantic analysis (LSA): It works on linear algebra (singular value decomposition)
  • Non-negative matrix factorization: It's based on linear algebra

We will primarily discuss LDA, which is considered the most popular of all. 

LDA is a matrix factorization technique that works on an assumption that documents are formed...