Large Scale Machine Learning with Python
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Numar articol:187839478
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Preț:377,00 Lei
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Anuntul a expirat la:22.12.2020, 15:00
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Learn to build powerful machine learning models quickly and deploy
large-scale predictive applications About This Book 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 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. 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 In Detail 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. Style and approach This efficient and practical title is
stuffed full of the techniques, tips and tools you need to ensure
your large scale Python machine learning runs swiftly and
seamlessly. Large-scale machine learning tackles a different issue
to what is currently on the market. Those working with Hadoop
clusters and in data intensive environments can now learn effective
ways of building powerful machine learning models from prototype to
production. This book is written in a style that programmers from
other languages (R, Julia, Java, Matlab) can follow.
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