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
About the Authors
About the Reviewer


In this chapter, we've introduced three popular unsupervised learners able to scale to cope with big data. The first, PCA, is able to reduce the number of features by creating ones containing the majority of variance (that is, the principal ones). K-means is a clustering algorithm able to group similar points together and associate them with a centroid. LDA is a powerful method to do topic modeling on textual data, that is, model the topics per document and the words appearing in a topic jointly.

In the next chapter, we will introduce some advanced and very recent methods of machine learning, still not part of the mainstream, naturally great for small datasets, but also suitable to process large scale machine learning.