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

Feature management with data streams


Data streams pose the problem that you cannot evaluate as you would do when working on a complete in-memory dataset. For a correct and optimal approach to feed your SGD out-of-core algorithm, you first have to survey the data (by taking a chuck of the initial instances of the file, for example) and find out the type of data you have at hand.

We distinguish among the following types of data:

  • Quantitative values

  • Categorical values encoded with integer numbers

  • Unstructured categorical values expressed in textual form

When data is quantitative, it could just be fed to the SGD learner but for the fact that the algorithm is quite sensitive to feature scaling; that is, you have to bring all the quantitative features into the same range of values or the learning process won't converge easily and correctly. Possible scaling strategies are converting all the values in the range [0,1], [-1,1] or standardizing the variable by centering its mean to zero and converting...