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

Support Vector Machines


Support Vector Machines (SVMs) are a set of supervised learning techniques for classification and regression (and also for outlier detection), which is quite versatile as it can fit both linear and nonlinear models thanks to the availability of special functions—kernel functions. The specialty of such kernel functions is to be able to map the input features into a new, more complex feature vector using a limited amount of computations. Kernel functions nonlinearly recombine the original features, making possible the mapping of the response by very complex functions. In such a sense, SVMs are comparable to neural networks as universal approximators, and thus can boast a similar predictive power in many problems.

Contrary to the linear models seen in the previous chapter, SVMs started as a method to solve classification problems, not regression ones.

SVMs were invented at the AT&T laboratories in the '90s by the mathematician, Vladimir Vapnik, and computer scientist...