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

Unsupervised methods

Unsupervised learning is a branch of machine learning whose algorithms reveal inferences from data without an explicit label (unlabeled data). The goal of such techniques is to extract hidden patterns and group similar data.

In these algorithms, the unknown parameters of interests of each observation (the group membership and topic composition, for instance) are often modeled as latent variables (or a series of hidden variables), hidden in the system of observed variables that cannot be observed directly, but only deduced from the past and present outputs of the system. Typically, the output of the system contains noise, which makes this operation harder.

In common problems, unsupervised methods are used in two main situations:

  • With labeled datasets to extract additional features to be processed by the classifier/regressor down to the processing chain. Enhanced by additional features, they may perform better.

  • With labeled or unlabeled datasets to extract some information...