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

Datasets to experiment with on your own


As in the previous chapter, we will be using datasets from the UCI Machine Learning Repository, in particular the bike-sharing dataset (a regression problem) and Forest Covertype Data (a multiclass classification problem).

If you have not done so before or if you need to download both the datasets again, you will need a couple of functions defined in the Datasets to try the real thing yourself section of Chapter 2, Scalable Learning in Scikit-learn. The needed functions are unzip_from_UCI and gzip_from_UCI. Both have a Python connect to the UCI repository; download a compressed file and unzip it in the working Python directory. If you call the functions from an IPython cell, you will find the necessary new directories and files exactly where IPython will look for them.

In case the functions do not work for you, never mind; we will provide you with the link for a direct download. After that, all you will have to do is unpack the data in the current working...