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 introductory chapter, we have illustrated the different ways in which we can make machine learning algorithms scalable using Python (scale up and scale out techniques). We also proposed some motivating examples and set the stage for the book by illustrating how to install Python on your machine. In particular, we introduced you to Jupyter and covered all the most important packages that will be used in this book.

In the next chapter, we will dive into discussing how stochastic gradient descent can help you deal with massive datasets by leveraging I/O on a single machine. Basically, we will cover different ways of streaming data from large files or data repositories and feed it into a basic learning algorithm. You will be amazed at how simple solutions can be effective, and you will discover that even your desktop computer can easily crunch big data.