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Build efficient data flow and machine learning programs with this
flexible, multi-functional open-source cluster-computing framework
Key Features Master the art of real-time big data processing and
machine learning Explore a wide range of use-cases to analyze large
data Discover ways to optimize your work by using many features of
Spark 2.x and Scala Book DescriptionApache Spark is an in-memory,
cluster-based data processing system that provides a wide range of
functionalities such as big data processing, analytics, machine
learning, and more. With this Learning Path, you can take your
knowledge of Apache Spark to the next level by learning how to
expand Spark's functionality and building your own data flow and
machine learning programs on this platform. You will work with the
different modules in Apache Spark, such as interactive querying
with Spark SQL, using DataFrames and datasets, implementing
streaming analytics with Spark Streaming, and applying machine
learning and deep learning techniques on Spark using MLlib and
various external tools. By the end of this elaborately designed
Learning Path, you will have all the knowledge you need to master
Apache Spark, and build your own big data processing and analytics
pipeline quickly and without any hassle. This Learning Path
includes content from the following Packt products: Mastering
Apache Spark 2.x by Romeo Kienzler Scala and Spark for Big Data
Analytics by Md. Rezaul Karim, Sridhar Alla Apache Spark 2.x
Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi
Rajendran, Broderick Hall, Shuen MeiCookbook What you will learn
Get to grips with all the features of Apache Spark 2.x Perform
highly optimized real-time big data processing Use ML and DL
techniques with Spark MLlib and third-party tools Analyze
structured and unstructured data using SparkSQL and GraphX
Understand tuning, debugging, and monitoring of big data
applications Build scalable and fault-tolerant streaming
applications Develop scalable recommendation engines Who this book
is forIf you are an intermediate-level Spark developer looking to
master the advanced capabilities and use-cases of Apache Spark 2.x,
this Learning Path is ideal for you. Big data professionals who
want to learn how to integrate and use the features of Apache Spark
and build a strong big data pipeline will also find this Learning
Path useful. To grasp the concepts explained in this Learning Path,
you must know the fundamentals of Apache Spark and Scala.
Simplify machine learning model implementations with Spark About
This Book * Solve the day-to-day problems of data science with
Spark * This unique cookbook consists of exciting and intuitive
numerical recipes * Optimize your work by acquiring, cleaning,
analyzing, predicting, and visualizing your data Who This Book Is
For This book is for Scala developers with a fairly good exposure
to and understanding of machine learning techniques, but lack
practical implementations with Spark. A solid knowledge of machine
learning algorithms is assumed, as well as hands-on experience of
implementing ML algorithms with Scala. However, you do not need to
be acquainted with the Spark ML libraries and ecosystem. What You
Will Learn * Get to know how Scala and Spark go hand-in-hand for
developers when developing ML systems with Spark * Build a
recommendation engine that scales with Spark * Find out how to
build unsupervised clustering systems to classify data in Spark *
Build machine learning systems with the Decision Tree and Ensemble
models in Spark * Deal with the curse of high-dimensionality in big
data using Spark * Implement Text analytics for Search Engines in
Spark * Streaming Machine Learning System implementation using
Spark In Detail Machine learning aims to extract knowledge from
data, relying on fundamental concepts in computer science,
statistics, probability, and optimization. Learning about
algorithms enables a wide range of applications, from everyday
tasks such as product recommendations and spam filtering to cutting
edge applications such as self-driving cars and personalized
medicine. You will gain hands-on experience of applying these
principles using Apache Spark, a resilient cluster computing system
well suited for large-scale machine learning tasks. This book
begins with a quick overview of setting up the necessary IDEs to
facilitate the execution of code examples that will be covered in
various chapters. It also highlights some key issues developers
face while working with machine learning algorithms on the Spark
platform. We progress by uncovering the various Spark APIs and the
implementation of ML algorithms with developing classification
systems, recommendation engines, text analytics, clustering, and
learning systems. Toward the final chapters, we'll focus on
building high-end applications and explain various unsupervised
methodologies and challenges to tackle when implementing with big
data ML systems. Style and approach This book is packed with
intuitive recipes supported with line-by-line explanations to help
you understand how to optimize your work flow and resolve problems
when working with complex data modeling tasks and predictive
algorithms. This is a valuable resource for data scientists and
those working on large scale data projects.
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