Large Scale Machine Learning with Python: Learn to build powerful machine learning models quickly and deploy large-scale predictive applications

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Key benefits

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

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

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

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Z.V. Sep 19, 2016

This is the best book for Python-based data science, focusing on ML and big data I have encountered (and I’ve been around!). The authors cover a wide-range of intermediate and advanced topics, which they explain in terms of theory and applications. I particularly liked the Unsupervised Learning chapter, where they not only covered the quite popular k-means algorithm, but also provided a couple of heuristics for finding the optimum number of clusters while they wrote a few words about one of its most powerful variants (k-means++) too.Although Python falls short when it comes to handling large data sets or multiple CPUs/GPUs on its own, the authors describe the various solutions to these issues via the use of large scale frameworks, such as Spark, making Python a versatile tool for big data scenarios. Also, they introduce the various packages required to accomplish all the analytics-related tasks, making this book also a great reference manual for all data scientists who veer towards this language.Personally I lean towards more elegant and more modern programming tools, such a s Julia and Scala, but I found this book quite refreshing and insightful, definitely a great addition to my data science library. If you are someone who takes data science seriously and has learned the basics, I would highly recommend this book for you.

Amazon Verified review

Oleg Okun Aug 21, 2016

Disclosure: I was a technical reviewer of this book.Many books when their subject is Machine Learning with Python concentrate on a few most known and used libraries to explain Machine Learning tasks and solutions. Although I don't want to say that such books are useless for readers, they may still leave gaps in understanding of how a certain method or library would work in real-world scenarios. Authors of the book "Large Scale Machine Learning with Python" set up an ambitious goal to teach readers how to solve real-world Machine Learning problems by employing a variety of libraries, frameworks, and tools relying on Python. This advantageously differentiates a given book from many other books on the same subject.The following practical situations are considered and their solutions are presented:- Tall datasets when the number of cases is large, compared to the number of features.- Wide datasets when the number of features is large, compared to the number of cases.- Both tall and wide datasets when both the number of features and the number of cases are large.- Sparse datasets when there are many zero-valued elements.The book treats the problem of scalability from different angles, such as fast batch (offline) processing, incremental online processing (one instance at a time arrives), streaming processing (a chunk of instances at a time arrives) and distributed processing. Popular libraries and frameworks, such as Gensim, H2O, XGBoost, TensorFlow, Theano, Theanets, Keras, Vowpal Wabbit, and Spark and their applications are explained through numerous Python snippets. In my opinion, this is one of the first books presenting all these tools under one cover.In addition to Python code, the book also covers such advanced topics like Deep Learning, Ensemble Learning, validation of streaming algorithm performance, and GPU processing.I recommend this book as a good companion to any Machine Learning practitioner who already has fairly good understanding of theory behind Machine Learning algorithms.

M. Athar Aug 31, 2017

This book is just too all over the place to be useful. Most of the stuff you can learn for free by going through the documentation for the various technologies discussed.No real discussion on RNNs, or calculus on computational graphs (which bascially defeats the purpose of tensorflow).

Large Scale Machine Learning with Python: Learn to build powerful machine learning models quickly and deploy large-scale predictive applications

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