
Large Scale Machine Learning with Python
Learn to build powerful machine learning models quickly and deploy large-scale predictive applications
Packt Publishing
Published on 3. August 2016
Book
Paperback/Softback
420 pages
978-1-78588-721-5 (ISBN)
Description
Learn to build powerful machine learning models quickly and deploy large-scale predictive applications
Key Features
[*]Design, engineer and deploy scalable machine learning solutions with the power of Python
[*]Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework
[*]Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale
Book DescriptionLarge 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
[*]Apply the most scalable machine learning algorithms
[*]Work with modern state-of-the-art large-scale machine learning techniques
[*]Increase predictive accuracy with deep learning and scalable data-handling techniques
[*]Improve your work by combining the MapReduce framework with Spark
[*]Build powerful ensembles at scale
[*] Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine
Who this book is forThis 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.
Key Features
[*]Design, engineer and deploy scalable machine learning solutions with the power of Python
[*]Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework
[*]Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale
Book DescriptionLarge 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
[*]Apply the most scalable machine learning algorithms
[*]Work with modern state-of-the-art large-scale machine learning techniques
[*]Increase predictive accuracy with deep learning and scalable data-handling techniques
[*]Improve your work by combining the MapReduce framework with Spark
[*]Build powerful ensembles at scale
[*] Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine
Who this book is forThis 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.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 23 mm
Weight
780 gr
ISBN-13
978-1-78588-721-5 (9781785887215)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Luca Massaron | Alberto Boschetti | Bastiaan Sjardin
Large Scale Machine Learning with Python
Learn to build powerful machine learning models quickly and deploy large-scale predictive applications
E-Book
06/2024
1st Edition
Packt Publishing Limited
from
€41.99
Available for download
Persons
Luca Massaron is a data scientist with over a decade of experience in transforming data into high-impact, innovative artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is the author of numerous bestselling books on AI, machine learning, and algorithms. Luca is also a 3x Kaggle Grandmaster who reached number 7 in the worldwide user rankings for his performance in data science competitions. Additionally, he is recognized as a Google Developer Expert (GDE) in AI, Kaggle, and the cloud. Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a Ph.D. in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from natural language processing (NLP) and behavioral analysis to machine learning and distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events. Bastiaan Sjardin is a data scientist and founder with a background in artificial intelligence and mathematics. He has a MSc degree in cognitive science obtained at the University of Leiden together with on campus courses at Massachusetts Institute of Technology (MIT). In the past 5 years, he has worked on a wide range of data science and artificial intelligence projects. He is a frequent community TA at Coursera in the social network analysis course from the University of Michigan and the practical machine learning course from Johns Hopkins University. His programming languages of choice are Python and R. Currently, he is the cofounder of Quandbee (http://www.quandbee.com/), a company providing machine learning and artificial intelligence applications at scale.
Content
Table of Contents
First Steps to Scalability
Scalable Learning in Scikit Learn
Fast learning SVM
Neural Networks & Deep Learning
Deep learning with Tensorflow
CART at scale
Unsupervised Learning at Scale
Distributed environments: Hadoop and Spark
Practical Machine Learning with Spark and Python
First Steps to Scalability
Scalable Learning in Scikit Learn
Fast learning SVM
Neural Networks & Deep Learning
Deep learning with Tensorflow
CART at scale
Unsupervised Learning at Scale
Distributed environments: Hadoop and Spark
Practical Machine Learning with Spark and Python