Mastering Scala Machine Learning

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Mastering Scala Machine Learning

Author : Alex Kozlov
Publisher : Packt Publishing Ltd
ISBN 13 : 178588526X
Total Pages : 310 pages
Book Rating : 4.7/5 (858 download)

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Book Synopsis Mastering Scala Machine Learning by : Alex Kozlov

Download or read book Mastering Scala Machine Learning written by Alex Kozlov and published by Packt Publishing Ltd. This book was released on 2016-06-28 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and Hadoop About This Book This is a primer on functional-programming-style techniques to help you efficiently process and analyze all of your data Get acquainted with the best and newest tools available such as Scala, Spark, Parquet and MLlib for machine learning Learn the best practices to incorporate new Big Data machine learning in your data-driven enterprise to gain future scalability and maintainability Who This Book Is For Mastering Scala Machine Learning is intended for enthusiasts who want to plunge into the new pool of emerging techniques for machine learning. Some familiarity with standard statistical techniques is required. What You Will Learn Sharpen your functional programming skills in Scala using REPL Apply standard and advanced machine learning techniques using Scala Get acquainted with Big Data technologies and grasp why we need a functional approach to Big Data Discover new data structures, algorithms, approaches, and habits that will allow you to work effectively with large amounts of data Understand the principles of supervised and unsupervised learning in machine learning Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet Construct reliable and robust data pipelines and manage data in a data-driven enterprise Implement scalable model monitoring and alerts with Scala In Detail Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala. Style and approach This hands-on guide dives straight into implementing Scala for machine learning without delving much into mathematical proofs or validations. There are ample code examples and tricks that will help you sail through using the standard techniques and libraries. This book provides practical examples from the field on how to correctly tackle data analysis problems, particularly for modern Big Data datasets.

Mastering Scala Machine Learning

Author : Alexander Kozlov
Publisher : Packt Publishing
ISBN 13 : 9781785880889
Total Pages : 310 pages
Book Rating : 4.8/5 (88 download)

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Book Synopsis Mastering Scala Machine Learning by : Alexander Kozlov

Download or read book Mastering Scala Machine Learning written by Alexander Kozlov and published by Packt Publishing. This book was released on 2016-06-29 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and HadoopAbout This Book*This is a primer on functional-programming-style techniques to help you efficiently process and analyze all of your data*Get acquainted with the best and newest tools available such as Scala, Spark, Parquet and MLlib for machine learning*Learn the best practices to incorporate new Big Data machine learning in your data-driven enterprise to gain future scalability and maintainabilityWho This Book Is ForMastering Scala Machine Learning is intended for enthusiasts who want to plunge into the new pool of emerging techniques for machine learning. Some familiarity with standard statistical techniques is required.What You Will Learn*Sharpen your functional programming skills in Scala using REPL*Apply standard and advanced machine learning techniques using Scala*Get acquainted with Big Data technologies and grasp why we need a functional approach to Big Data*Discover new data structures, algorithms, approaches, and habits that will allow you to work effectively with large amounts of data*Understand the principles of supervised and unsupervised learning in machine learning*Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet*Construct reliable and robust data pipelines and manage data in a data-driven enterprise*Implement scalable model monitoring and alerts with ScalaIn DetailSince the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing.This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees.Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.

Scala:Applied Machine Learning

Author : Pascal Bugnion
Publisher :
ISBN 13 : 9781787126640
Total Pages : 1265 pages
Book Rating : 4.1/5 (266 download)

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Book Synopsis Scala:Applied Machine Learning by : Pascal Bugnion

Download or read book Scala:Applied Machine Learning written by Pascal Bugnion and published by . This book was released on 2017-02-23 with total page 1265 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of Scala and master the art of building, improving, and validating scalable machine learning and AI applications using Scala's most advanced and finest featuresAbout This Book- Build functional, type-safe routines to interact with relational and NoSQL databases with the help of the tutorials and examples provided- Leverage your expertise in Scala programming to create and customize your own scalable machine learning algorithms - Experiment with different techniques; evaluate their benefits and limitations using real-world financial applications - Get to know the best practices to incorporate new Big Data machine learning in your data-driven enterprise and gain future scalability and maintainabilityWho This Book Is ForThis Learning Path is for engineers and scientists who are familiar with Scala and want to learn how to create, validate, and apply machine learning algorithms. It will also benefit software developers with a background in Scala programming who want to apply machine learning.What You Will Learn- Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations- Deploy scalable parallel applications using Apache Spark, loading data from HDFS or Hive- Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters- Apply key learning strategies to perform technical analysis of financial markets- Understand the principles of supervised and unsupervised learning in machine learning- Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet- Construct reliable and robust data pipelines and manage data in a data-driven enterprise- Implement scalable model monitoring and alerts with ScalaIn DetailThis Learning Path aims to put the entire world of machine learning with Scala in front of you. Scala for Data Science, the first module in this course, is a tutorial guide that provides tutorials on some of the most common Scala libraries for data science, allowing you to quickly get up to speed building data science and data engineering solutions.The second course, Scala for Machine Learning guides you through the process of building AI applications with diagrams, formal mathematical notation, source code snippets, and useful tips. A review of the Akka framework and Apache Spark clusters concludes the tutorial.The next module, Mastering Scala Machine Learning, is the final step in this course. It will take your knowledge to next level and help you use the knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees.By the end of this course, you will be a master at Scala machine learning and have enough expertise to be able to build complex machine learning projects using Scala.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:- Scala for Data Science, Pascal Bugnion- Scala for Machine Learning, Patrick Nicolas- Mastering Scala Machine Learning, Alex KozlovStyle and approachA tutorial with complete examples, this course will give you the tools to start building useful data engineering and data science solutions straightaway. This course provides practical examples from the field on how to correctly tackle data analysis problems, particularly for modern Big Data datasets.

Scala Machine Learning Projects

Author : Md. Rezaul Karim
Publisher : Packt Publishing Ltd
ISBN 13 : 1788471474
Total Pages : 461 pages
Book Rating : 4.7/5 (884 download)

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Book Synopsis Scala Machine Learning Projects by : Md. Rezaul Karim

Download or read book Scala Machine Learning Projects written by Md. Rezaul Karim and published by Packt Publishing Ltd. This book was released on 2018-01-31 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming. Key Features Explore machine learning techniques with prominent open source Scala libraries such as Spark ML, H2O, MXNet, Zeppelin, and DeepLearning4j Solve real-world machine learning problems by delving complex numerical computing with Scala functional programming in a scalable and faster way Cover all key aspects such as collection, storing, processing, analyzing, and evaluation required to build and deploy machine models on computing clusters using Scala Play framework. Book Description Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development. If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet. At the end, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment. What you will learn Apply advanced regression techniques to boost the performance of predictive models Use different classification algorithms for business analytics Generate trading strategies for Bitcoin and stock trading using ensemble techniques Train Deep Neural Networks (DNN) using H2O and Spark ML Utilize NLP to build scalable machine learning models Learn how to apply reinforcement learning algorithms such as Q-learning for developing ML application Learn how to use autoencoders to develop a fraud detection application Implement LSTM and CNN models using DeepLearning4j and MXNet Who this book is for If you want to leverage the power of both Scala and Spark to make sense of Big Data, then this book is for you. If you are well versed with machine learning concepts and wants to expand your knowledge by delving into the practical implementation using the power of Scala, then this book is what you need! Strong understanding of Scala Programming language is recommended. Basic familiarity with machine Learning techniques will be more helpful.

Machine Learning with Scala Quick Start Guide

Author : Md. Rezaul Karim
Publisher : Packt Publishing Ltd
ISBN 13 : 1789345413
Total Pages : 215 pages
Book Rating : 4.7/5 (893 download)

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Book Synopsis Machine Learning with Scala Quick Start Guide by : Md. Rezaul Karim

Download or read book Machine Learning with Scala Quick Start Guide written by Md. Rezaul Karim and published by Packt Publishing Ltd. This book was released on 2019-04-30 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supervised and unsupervised machine learning made easy in Scala with this quick-start guide. Key FeaturesConstruct and deploy machine learning systems that learn from your data and give accurate predictionsUnleash the power of Spark ML along with popular machine learning algorithms to solve complex tasks in Scala.Solve hands-on problems by combining popular neural network architectures such as LSTM and CNN using Scala with DeepLearning4j libraryBook Description Scala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala. The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naïve Bayes algorithms. It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala. What you will learnGet acquainted with JVM-based machine learning libraries for Scala such as Spark ML and Deeplearning4jLearn RDDs, DataFrame, and Spark SQL for analyzing structured and unstructured dataUnderstand supervised and unsupervised learning techniques with best practices and pitfallsLearn classification and regression analysis with linear regression, logistic regression, Naïve Bayes, support vector machine, and tree-based ensemble techniques Learn effective ways of clustering analysis with dimensionality reduction techniquesLearn recommender systems with collaborative filtering approachDelve into deep learning and neural network architecturesWho this book is for This book is for machine learning developers looking to train machine learning models in Scala without spending too much time and effort. Some fundamental knowledge of Scala programming and some basics of statistics and linear algebra is all you need to get started with this book.

Scala for Machine Learning

Author : Patrick R. Nicolas
Publisher : Packt Publishing Ltd
ISBN 13 : 178712620X
Total Pages : 740 pages
Book Rating : 4.7/5 (871 download)

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Book Synopsis Scala for Machine Learning by : Patrick R. Nicolas

Download or read book Scala for Machine Learning written by Patrick R. Nicolas and published by Packt Publishing Ltd. This book was released on 2017-09-26 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage Scala and Machine Learning to study and construct systems that can learn from data About This Book Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulation, and updated source code in Scala Take your expertise in Scala programming to the next level by creating and customizing AI applications Experiment with different techniques and evaluate their benefits and limitations using real-world applications in a tutorial style Who This Book Is For If you're a data scientist or a data analyst with a fundamental knowledge of Scala who wants to learn and implement various Machine learning techniques, this book is for you. All you need is a good understanding of the Scala programming language, a basic knowledge of statistics, a keen interest in Big Data processing, and this book! What You Will Learn Build dynamic workflows for scientific computing Leverage open source libraries to extract patterns from time series Write your own classification, clustering, or evolutionary algorithm Perform relative performance tuning and evaluation of Spark Master probabilistic models for sequential data Experiment with advanced techniques such as regularization and kernelization Dive into neural networks and some deep learning architecture Apply some basic multiarm-bandit algorithms Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters Apply key learning strategies to a technical analysis of financial markets In Detail The discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, engineering design, logistics, manufacturing, and trading strategies, to detection of genetic anomalies. The book is your one stop guide that introduces you to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits. You start by learning data preprocessing and filtering techniques. Following this, you'll move on to unsupervised learning techniques such as clustering and dimension reduction, followed by probabilistic graphical models such as Naive Bayes, hidden Markov models and Monte Carlo inference. Further, it covers the discriminative algorithms such as linear, logistic regression with regularization, kernelization, support vector machines, neural networks, and deep learning. You'll move on to evolutionary computing, multibandit algorithms, and reinforcement learning. Finally, the book includes a comprehensive overview of parallel computing in Scala and Akka followed by a description of Apache Spark and its ML library. With updated codes based on the latest version of Scala and comprehensive examples, this book will ensure that you have more than just a solid fundamental knowledge in machine learning with Scala. Style and approach This book is designed as a tutorial with hands-on exercises using technical analysis of financial markets and corporate data. The approach of each chapter is such that it allows you to understand key concepts easily.

Mastering Machine Learning with Spark 2.x

Author : Alex Tellez
Publisher : Packt Publishing Ltd
ISBN 13 : 1785282417
Total Pages : 334 pages
Book Rating : 4.7/5 (852 download)

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Book Synopsis Mastering Machine Learning with Spark 2.x by : Alex Tellez

Download or read book Mastering Machine Learning with Spark 2.x written by Alex Tellez and published by Packt Publishing Ltd. This book was released on 2017-08-31 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial About This Book Process and analyze big data in a distributed and scalable way Write sophisticated Spark pipelines that incorporate elaborate extraction Build and use regression models to predict flight delays Who This Book Is For Are you a developer with a background in machine learning and statistics who is feeling limited by the current slow and “small data” machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark. What You Will Learn Use Spark streams to cluster tweets online Run the PageRank algorithm to compute user influence Perform complex manipulation of DataFrames using Spark Define Spark pipelines to compose individual data transformations Utilize generated models for off-line/on-line prediction Transfer the learning from an ensemble to a simpler Neural Network Understand basic graph properties and important graph operations Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language Use K-means algorithm to cluster movie reviews dataset In Detail The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment. Style and approach This book takes a practical approach to help you get to grips with using Spark for analytics and to implement machine learning algorithms. We'll teach you about advanced applications of machine learning through illustrative examples. These examples will equip you to harness the potential of machine learning, through Spark, in a variety of enterprise-grade systems.

Scala for Machine Learning, Second Edition

Author : Patrick R. Nicolas
Publisher : Packt Publishing
ISBN 13 : 9781787122383
Total Pages : 740 pages
Book Rating : 4.1/5 (223 download)

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Book Synopsis Scala for Machine Learning, Second Edition by : Patrick R. Nicolas

Download or read book Scala for Machine Learning, Second Edition written by Patrick R. Nicolas and published by Packt Publishing. This book was released on 2017-09-26 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage Scala and Machine Learning to study and construct systems that can learn from dataAbout This Book* Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulation, and updated source code in Scala* Take your expertise in Scala programming to the next level by creating and customizing AI applications* Experiment with different techniques and evaluate their benefits and limitations using real-world applications in a tutorial styleWho This Book Is ForIf you're a data scientist or a data analyst with a fundamental knowledge of Scala who wants to learn and implement various Machine learning techniques, this book is for you. All you need is a good understanding of the Scala programming language, a basic knowledge of statistics, a keen interest in Big Data processing, and this book!What You Will Learn* Build dynamic workflows for scientific computing* Leverage open source libraries to extract patterns from time series* Write your own classification, clustering, or evolutionary algorithm* Perform relative performance tuning and evaluation of Spark* Master probabilistic models for sequential data* Experiment with advanced techniques such as regularization and kernelization* Dive into neural networks and some deep learning architecture* Apply some basic multiarm-bandit algorithms* Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters* Apply key learning strategies to a technical analysis of financial marketsIn DetailThe discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, engineering design, logistics, manufacturing, and trading strategies, to detection of genetic anomalies.The book is your one stop guide that introduces you to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits. You start by learning data preprocessing and filtering techniques. Following this, you'll move on to unsupervised learning techniques such as clustering and dimension reduction, followed by probabilistic graphical models such as Naive Bayes, hidden Markov models and Monte Carlo inference. Further, it covers the discriminative algorithms such as linear, logistic regression with regularization, kernelization, support vector machines, neural networks, and deep learning. You'll move on to evolutionary computing, multibandit algorithms, and reinforcement learning.Finally, the book includes a comprehensive overview of parallel computing in Scala and Akka followed by a description of Apache Spark and its ML library. With updated codes based on the latest version of Scala and comprehensive examples, this book will ensure that you have more than just a solid fundamental knowledge in machine learning with Scala.Style and approachThis book is designed as a tutorial with hands-on exercises using technical analysis of financial markets and corporate data. The approach of each chapter is such that it allows you to understand key concepts easily.

Scala and Spark for Big Data Analytics

Author : Md. Rezaul Karim
Publisher : Packt Publishing Ltd
ISBN 13 : 1783550503
Total Pages : 786 pages
Book Rating : 4.7/5 (835 download)

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Book Synopsis Scala and Spark for Big Data Analytics by : Md. Rezaul Karim

Download or read book Scala and Spark for Big Data Analytics written by Md. Rezaul Karim and published by Packt Publishing Ltd. This book was released on 2017-07-25 with total page 786 pages. Available in PDF, EPUB and Kindle. Book excerpt: Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye! About This Book Learn Scala's sophisticated type system that combines Functional Programming and object-oriented concepts Work on a wide array of applications, from simple batch jobs to stream processing and machine learning Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark Who This Book Is For Anyone who wishes to learn how to perform data analysis by harnessing the power of Spark will find this book extremely useful. No knowledge of Spark or Scala is assumed, although prior programming experience (especially with other JVM languages) will be useful to pick up concepts quicker. What You Will Learn Understand object-oriented & functional programming concepts of Scala In-depth understanding of Scala collection APIs Work with RDD and DataFrame to learn Spark's core abstractions Analysing structured and unstructured data using SparkSQL and GraphX Scalable and fault-tolerant streaming application development using Spark structured streaming Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML Build clustering models to cluster a vast amount of data Understand tuning, debugging, and monitoring Spark applications Deploy Spark applications on real clusters in Standalone, Mesos, and YARN In Detail Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big. Style and approach Filled with practical examples and use cases, this book will hot only help you get up and running with Spark, but will also take you farther down the road to becoming a data scientist.

Mastering Apache Spark 2.x

Author : Romeo Kienzler
Publisher : Packt Publishing Ltd
ISBN 13 : 178528522X
Total Pages : 354 pages
Book Rating : 4.7/5 (852 download)

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Book Synopsis Mastering Apache Spark 2.x by : Romeo Kienzler

Download or read book Mastering Apache Spark 2.x written by Romeo Kienzler and published by Packt Publishing Ltd. This book was released on 2017-07-26 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced analytics on your Big Data with latest Apache Spark 2.x About This Book An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities. Extend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark. Master the art of real-time processing with the help of Apache Spark 2.x Who This Book Is For If you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected. What You Will Learn Examine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4J Study highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming Evaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames Apply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud Understand internal details of cost based optimizers used in Catalyst, SystemML and GraphFrames Learn how specific parameter settings affect overall performance of an Apache Spark cluster Leverage Scala, R and python for your data science projects In Detail Apache Spark is an in-memory cluster-based parallel processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and SQL. This book aims to take your knowledge of Spark to the next level by teaching you how to expand Spark's functionality and implement your data flows and machine/deep learning programs on top of the platform. The book commences with an overview of the Spark ecosystem. It will introduce you to Project Tungsten and Catalyst, two of the major advancements of Apache Spark 2.x. You will understand how memory management and binary processing, cache-aware computation, and code generation are used to speed things up dramatically. The book extends to show how to incorporate H20, SystemML, and Deeplearning4j for machine learning, and Jupyter Notebooks and Kubernetes/Docker for cloud-based Spark. During the course of the book, you will learn about the latest enhancements to Apache Spark 2.x, such as interactive querying of live data and unifying DataFrames and Datasets. You will also learn about the updates on the APIs and how DataFrames and Datasets affect SQL, machine learning, graph processing, and streaming. You will learn to use Spark as a big data operating system, understand how to implement advanced analytics on the new APIs, and explore how easy it is to use Spark in day-to-day tasks. Style and approach This book is an extensive guide to Apache Spark modules and tools and shows how Spark's functionality can be extended for real-time processing and storage with worked examples.

Mastering Scala

Author : Cybellium Ltd
Publisher : Cybellium Ltd
ISBN 13 :
Total Pages : 263 pages
Book Rating : 4.8/5 (69 download)

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Book Synopsis Mastering Scala by : Cybellium Ltd

Download or read book Mastering Scala written by Cybellium Ltd and published by Cybellium Ltd. This book was released on 2023-09-26 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you ready to dive into the world of advanced programming with confidence and expertise? "Mastering Scala" is your gateway to unlocking the true power of the Scala programming language. Whether you're an experienced developer seeking to expand your horizons or a programming enthusiast ready to embark on a transformative journey, this comprehensive guide will equip you with the skills to develop elegant, scalable, and high-performance software. Key Features: 1. In-Depth Exploration of Scala Fundamentals: Immerse yourself in the core concepts of Scala programming, from its unique blend of object-oriented and functional paradigms to its expressive syntax. Build a strong foundation that enables you to tackle complex programming challenges. 2. Functional Programming Mastery: Discover the beauty of functional programming in Scala. Learn how to leverage higher-order functions, immutability, and pattern matching to create clean, maintainable code that is both concise and powerful. 3. Concurrency and Parallelism: Dive into Scala's concurrent and parallel programming capabilities. Explore actors, Futures, and parallel collections to build responsive, highly performant applications that excel in a multi-core world. 4. Advanced Data Structures and Algorithms: Elevate your programming skills by mastering advanced data structures and algorithms in Scala. From sets and maps to trees and graphs, learn how to solve intricate problems using Scala's powerful abstractions. 5. Building Robust Applications: Explore best practices for structuring and organizing your Scala projects. Gain insights into error handling, testing, and writing code that is not only functional but also robust and easy to maintain. 6. Leveraging Scala's Ecosystem: Maximize your productivity by exploring the vibrant ecosystem of Scala libraries and frameworks. From web development to data analysis, discover tools that will help you create software efficiently and effectively. 7. Type System and Advanced Language Features: Dive into Scala's sophisticated type system and explore advanced language features like implicits and type classes. Craft expressive, type-safe code that reflects the elegance of Scala. 8. Performance Optimization: Master the art of optimizing Scala applications for top-notch performance. Learn profiling techniques, memory management, and concurrency tuning to ensure your software runs efficiently. 9. Deployment and DevOps: Navigate the landscape of deploying Scala applications to various environments. Discover containerization and adopt DevOps practices that streamline your development-to-production pipeline. Who This Book Is For: "Mastering Scala" is an indispensable companion for developers of all skill levels who are passionate about mastering the Scala programming language. Whether you're a novice programmer or an experienced coder eager to embrace Scala's unique features, this book will guide you through the language's intricacies and empower you to create sophisticated, high-performance software.

Machine Learning with Spark

Author : Rajdeep Dua
Publisher : Packt Publishing Ltd
ISBN 13 : 1785886428
Total Pages : 523 pages
Book Rating : 4.7/5 (858 download)

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Book Synopsis Machine Learning with Spark by : Rajdeep Dua

Download or read book Machine Learning with Spark written by Rajdeep Dua and published by Packt Publishing Ltd. This book was released on 2017-04-28 with total page 523 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create scalable machine learning applications to power a modern data-driven business using Spark 2.x About This Book Get to the grips with the latest version of Apache Spark Utilize Spark's machine learning library to implement predictive analytics Leverage Spark's powerful tools to load, analyze, clean, and transform your data Who This Book Is For If you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages. What You Will Learn Get hands-on with the latest version of Spark ML Create your first Spark program with Scala and Python Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2 Access public machine learning datasets and use Spark to load, process, clean, and transform data Use Spark's machine learning library to implement programs by utilizing well-known machine learning models Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models Write Spark functions to evaluate the performance of your machine learning models In Detail This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business. Style and approach This practical tutorial with real-world use cases enables you to develop your own machine learning systems with Spark. The examples will help you combine various techniques and models into an intelligent machine learning system.

Mastering Machine Learning on AWS

Author : Dr. Saket S.R. Mengle
Publisher : Packt Publishing Ltd
ISBN 13 : 1789347505
Total Pages : 293 pages
Book Rating : 4.7/5 (893 download)

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Book Synopsis Mastering Machine Learning on AWS by : Dr. Saket S.R. Mengle

Download or read book Mastering Machine Learning on AWS written by Dr. Saket S.R. Mengle and published by Packt Publishing Ltd. This book was released on 2019-05-20 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key FeaturesBuild machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlowLearn model optimization, and understand how to scale your models using simple and secure APIsDevelop, train, tune and deploy neural network models to accelerate model performance in the cloudBook Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learnManage AI workflows by using AWS cloud to deploy services that feed smart data productsUse SageMaker services to create recommendation modelsScale model training and deployment using Apache Spark on EMRUnderstand how to cluster big data through EMR and seamlessly integrate it with SageMakerBuild deep learning models on AWS using TensorFlow and deploy them as servicesEnhance your apps by combining Apache Spark and Amazon SageMakerWho this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.

Scala: Guide for Data Science Professionals

Author : Pascal Bugnion
Publisher : Packt Publishing Ltd
ISBN 13 : 1787281035
Total Pages : 1101 pages
Book Rating : 4.7/5 (872 download)

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Book Synopsis Scala: Guide for Data Science Professionals by : Pascal Bugnion

Download or read book Scala: Guide for Data Science Professionals written by Pascal Bugnion and published by Packt Publishing Ltd. This book was released on 2017-02-24 with total page 1101 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scala will be a valuable tool to have on hand during your data science journey for everything from data cleaning to cutting-edge machine learning About This Book Build data science and data engineering solutions with ease An in-depth look at each stage of the data analysis process — from reading and collecting data to distributed analytics Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulations, and source code Who This Book Is For This learning path is perfect for those who are comfortable with Scala programming and now want to enter the field of data science. Some knowledge of statistics is expected. What You Will Learn Transfer and filter tabular data to extract features for machine learning Read, clean, transform, and write data to both SQL and NoSQL databases Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations Load data from HDFS and HIVE with ease Run streaming and graph analytics in Spark for exploratory analysis Bundle and scale up Spark jobs by deploying them into a variety of cluster managers Build dynamic workflows for scientific computing Leverage open source libraries to extract patterns from time series Master probabilistic models for sequential data In Detail Scala is especially good for analyzing large sets of data as the scale of the task doesn't have any significant impact on performance. Scala's powerful functional libraries can interact with databases and build scalable frameworks — resulting in the creation of robust data pipelines. The first module introduces you to Scala libraries to ingest, store, manipulate, process, and visualize data. Using real world examples, you will learn how to design scalable architecture to process and model data — starting from simple concurrency constructs and progressing to actor systems and Apache Spark. After this, you will also learn how to build interactive visualizations with web frameworks. Once you have become familiar with all the tasks involved in data science, you will explore data analytics with Scala in the second module. You'll see how Scala can be used to make sense of data through easy to follow recipes. You will learn about Bokeh bindings for exploratory data analysis and quintessential machine learning with algorithms with Spark ML library. You'll get a sufficient understanding of Spark streaming, machine learning for streaming data, and Spark graphX. Armed with a firm understanding of data analysis, you will be ready to explore the most cutting-edge aspect of data science — machine learning. The final module teaches you the A to Z of machine learning with Scala. You'll explore Scala for dependency injections and implicits, which are used to write machine learning algorithms. You'll also explore machine learning topics such as clustering, dimentionality reduction, Naive Bayes, Regression models, SVMs, neural networks, and more. This learning path combines some of the best that Packt has to offer into one complete, curated package. It includes content from the following Packt products: Scala for Data Science, Pascal Bugnion Scala Data Analysis Cookbook, Arun Manivannan Scala for Machine Learning, Patrick R. Nicolas Style and approach A complete package with all the information necessary to start building useful data engineering and data science solutions straight away. It contains a diverse set of recipes that cover the full spectrum of interesting data analysis tasks and will help you revolutionize your data analysis skills using Scala.

Scala: Guide for Data Science Professionals

Author : Pascal Bugnion
Publisher :
ISBN 13 : 9781787282858
Total Pages : 1101 pages
Book Rating : 4.2/5 (828 download)

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Book Synopsis Scala: Guide for Data Science Professionals by : Pascal Bugnion

Download or read book Scala: Guide for Data Science Professionals written by Pascal Bugnion and published by . This book was released on 2017 with total page 1101 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Mastering Machine Learning Algorithms

Author : Giuseppe Bonaccorso
Publisher : Packt Publishing Ltd
ISBN 13 : 1838821910
Total Pages : 799 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Mastering Machine Learning Algorithms by : Giuseppe Bonaccorso

Download or read book Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 799 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key FeaturesUpdated to include new algorithms and techniquesCode updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applicationsBook Description Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios. What you will learnUnderstand the characteristics of a machine learning algorithmImplement algorithms from supervised, semi-supervised, unsupervised, and RL domainsLearn how regression works in time-series analysis and risk predictionCreate, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANsWho this book is for This book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.

Mastering Java for Data Science

Author : Alexey Grigorev
Publisher : Packt Publishing Ltd
ISBN 13 : 1785887394
Total Pages : 355 pages
Book Rating : 4.7/5 (858 download)

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Book Synopsis Mastering Java for Data Science by : Alexey Grigorev

Download or read book Mastering Java for Data Science written by Alexey Grigorev and published by Packt Publishing Ltd. This book was released on 2017-04-27 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use Java to create a diverse range of Data Science applications and bring Data Science into production About This Book An overview of modern Data Science and Machine Learning libraries available in Java Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks. Easy-to-follow illustrations and the running example of building a search engine. Who This Book Is For This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you! What You Will Learn Get a solid understanding of the data processing toolbox available in Java Explore the data science ecosystem available in Java Find out how to approach different machine learning problems with Java Process unstructured information such as natural language text or images Create your own search engine Get state-of-the-art performance with XGBoost Learn how to build deep neural networks with DeepLearning4j Build applications that scale and process large amounts of data Deploy data science models to production and evaluate their performance In Detail Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings. Style and approach This is a practical guide where all the important concepts such as classification, regression, and dimensionality reduction are explained with the help of examples.