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

Stochastic learning

Having defined the streaming process, it is now time to glance at the learning process as it is the learning and its specific needs that determine the best way to handle data and transform it in the preprocessing phase.

Online learning, contrary to batch learning, works with a larger number of iterations and gets directions from each single instance at a time, thus allowing a more erratic learning procedure than an optimization made on a batch, which immediately tends to get the right direction expressed from the data as a whole.

Batch gradient descent

The core algorithm for machine learning, gradient descent, is therefore revisited in order to adapt to online learning. When working on batch data, gradient descent can minimize the cost function of a linear regression analysis using much less computations than statistical algorithms. The complexity of gradient descent ranks in the order O(n*p), making learning regression coefficients feasible even in the occurrence of a large...