Book Image

Large Scale Machine Learning with Python

By : Bastiaan Sjardin, Alberto Boschetti
Book Image

Large Scale Machine Learning with Python

By: Bastiaan Sjardin, Alberto Boschetti

Overview of this book

Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.
Table of Contents (17 chapters)
Large Scale Machine Learning with Python
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

LDA


LDA stands for Latent Dirichlet Allocation, and it's one of the widely-used techniques to analyze collections of textual documents.

Note

LDA is an acronym also used by another technique, Linear Discriminant Analysis, which is a supervised method for classification. Pay attention to how LDA is used as there's no connection between these two algorithms.

A full mathematical explanation of LDA would require the knowledge of probabilistic modeling, which is beyond the scope of this practical book. Here, instead, we will give you the most important intuitions behind the model and how to practically apply this model on a massive dataset.

First at all, LDA is used in a branch of data science named text mining, where the focus is on building learners to understand the natural language, for instance, based on textual examples. Specifically, LDA belongs to the category of topic-modeling algorithms as it tries to model the topics included in a document. Ideally, LDA is able to understand whether a document...