Large Scale Machine Learning with Python Contributor(s): Sjardin, Bastiaan (Author), Massaron, Luca (Author), Boschetti, Alberto (Author) |
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ISBN: 1785887211 ISBN-13: 9781785887215 Publisher: Packt Publishing OUR PRICE: $52.24 Product Type: Paperback - Other Formats Published: July 2016 |
Additional Information |
BISAC Categories: - Computers | Databases - Data Mining - Computers | Machine Theory - Computers | Neural Networks |
Physical Information: 0.86" H x 7.5" W x 9.25" (1.58 lbs) 420 pages |
Descriptions, Reviews, Etc. |
Publisher Description: Learn to build powerful machine learning models quickly and deploy large-scale predictive applications Key Features
Book Description 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. What you will learn
Who this book is for This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful. |