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 have seen how learning is possible out-of-core by streaming data, no matter how big it is, from a text file or database on your hard disk. These methods certainly apply to much bigger datasets than the examples that we used to demonstrate them (which actually could be solved in-memory using non-average, powerful hardware).

We also explained the core algorithm that makes out-of-core learning possible—SGD—and we examined its strength and weakness, emphasizing the necessity of streams to be really stochastic (which means in a random order) to be really effective, unless the order is part of the learning objectives. In particular, we introduced the Scikit-learn implementation of SGD, limiting our focus to the linear and logistic regression loss functions.

Finally, we discussed data preparation, introduced the hashing trick and validation strategies for streams, and wrapped up the acquired knowledge on SGD fitting two different models—classification and regression.