Building a Recommendation System with R
A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems. The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system. ...
A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems. The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system. ...
Numerical Python
Leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, SciPy, SymPy, Matplotlib, Pandas, and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more. After reading and using Numerical Python, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computational methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. ...
Leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, SciPy, SymPy, Matplotlib, Pandas, and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more. After reading and using Numerical Python, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computational methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. ...
Spark for Python Developers
Looking for a cluster computing system that provides high-level APIs? Apache Spark is your answer—an open source, fast, and general purpose cluster computing system. Spark's multi-stage memory primitives provide performance up to 100 times faster than Hadoop, and it is also well-suited for machine learning algorithms. Are you a Python developer inclined to work with Spark engine? If so, this book will be your companion as you create data-intensive app using Spark as a processing engine, Python visualization libraries, and web frameworks such as Flask. To begin with, you will learn the most effective way to install the Python development environment powered by Spark, Blaze, and Bookeh. You will then find out how to connect with data stores such as MySQL, MongoDB, Cassandra, and Hadoop. You'll expand your skills throughout, getting familiarized with the various data sources (Github, Twitter, Meetup, and Blogs), their data structures, and solutions to effectively tackle complex ...
Looking for a cluster computing system that provides high-level APIs? Apache Spark is your answer—an open source, fast, and general purpose cluster computing system. Spark's multi-stage memory primitives provide performance up to 100 times faster than Hadoop, and it is also well-suited for machine learning algorithms. Are you a Python developer inclined to work with Spark engine? If so, this book will be your companion as you create data-intensive app using Spark as a processing engine, Python visualization libraries, and web frameworks such as Flask. To begin with, you will learn the most effective way to install the Python development environment powered by Spark, Blaze, and Bookeh. You will then find out how to connect with data stores such as MySQL, MongoDB, Cassandra, and Hadoop. You'll expand your skills throughout, getting familiarized with the various data sources (Github, Twitter, Meetup, and Blogs), their data structures, and solutions to effectively tackle complex ...
OpenCV By Example
Open CV is a cross-platform, free-for-use library that is primarily used for real-time Computer Vision and image processing. It is considered to be one of the best open source libraries that helps developers focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you are completely new to the concept of Computer Vision or have a basic understanding of it, this book will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects. Starting from the installation of OpenCV on your system and understanding the basics of image processing, we swiftly move on to creating optical flow video analysis or text recognition in complex scenes, and will take you through the commonly used Computer Vision techniques to build your own Open CV projects from scratch. By the end of this book, you will be familiar with the basics of Open CV such as matrix operations, filters, and hist ...
Open CV is a cross-platform, free-for-use library that is primarily used for real-time Computer Vision and image processing. It is considered to be one of the best open source libraries that helps developers focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you are completely new to the concept of Computer Vision or have a basic understanding of it, this book will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects. Starting from the installation of OpenCV on your system and understanding the basics of image processing, we swiftly move on to creating optical flow video analysis or text recognition in complex scenes, and will take you through the commonly used Computer Vision techniques to build your own Open CV projects from scratch. By the end of this book, you will be familiar with the basics of Open CV such as matrix operations, filters, and hist ...
Big Data Analytics with Spark
This book is a step-by-step guide for learning how to use Spark for different types of big-data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, MLlib, and Spark ML. Big Data Analytics with Spark shows you how to use Spark and leverage its easy-to-use features to increase your productivity. You learn to perform fast data analysis using its in-memory caching and advanced execution engine, employ in-memory computing capabilities for building high-performance machine learning and low-latency interactive analytics applications, and much more. Moreover, the book shows you how to use Spark as a single integrated platform for a variety of data processing tasks, including ETL pipelines, BI, live data stream processing, graph analytics, and machine learning. The book also includes a chapter on Scala, the hottest functional programming l ...
This book is a step-by-step guide for learning how to use Spark for different types of big-data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, MLlib, and Spark ML. Big Data Analytics with Spark shows you how to use Spark and leverage its easy-to-use features to increase your productivity. You learn to perform fast data analysis using its in-memory caching and advanced execution engine, employ in-memory computing capabilities for building high-performance machine learning and low-latency interactive analytics applications, and much more. Moreover, the book shows you how to use Spark as a single integrated platform for a variety of data processing tasks, including ETL pipelines, BI, live data stream processing, graph analytics, and machine learning. The book also includes a chapter on Scala, the hottest functional programming l ...
Scalable Big Data Architecture
This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance. Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution. When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it's often necessary to delegate the load to Hadoop or Spark and use the No-SQL to serve processed dat ...
This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance. Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution. When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it's often necessary to delegate the load to Hadoop or Spark and use the No-SQL to serve processed dat ...
IPython Interactive Computing and Visualization Cookbook
IPython is at the heart of the Python scientific stack. With its widely acclaimed web-based notebook, IPython is today an ideal gateway to data analysis and numerical computing in Python. IPython Interactive Computing and Visualization Cookbook contains many ready-to-use focused recipes for high-performance scientific computing and data analysis. The first part covers programming techniques, including code quality and reproducibility; code optimization; high-performance computing through dynamic compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics. ...
IPython is at the heart of the Python scientific stack. With its widely acclaimed web-based notebook, IPython is today an ideal gateway to data analysis and numerical computing in Python. IPython Interactive Computing and Visualization Cookbook contains many ready-to-use focused recipes for high-performance scientific computing and data analysis. The first part covers programming techniques, including code quality and reproducibility; code optimization; high-performance computing through dynamic compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics. ...
Mastering Apache Spark
Apache Spark is an in-memory cluster based parallel processing system that provides a wide range of functionality like graph processing, machine learning, stream processing and SQL. It operates at unprecedented speeds, is easy to use and offers a rich set of data transformations. This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand Spark functionality. The book commences with an overview of the Spark eco-system. You will learn how to use MLlib to create a fully working neural net for handwriting recognition. You will then discover how stream processing can be tuned for optimal performance and to ensure parallel processing. The book extends to show how to incorporate H20 for machine learning, Titan for graph based storage, Databricks for cloud-based Spark. Intermediate Scala based code examples are provided for Apache Spark module processing in a CentOS Linux and Databricks cloud environment. ...
Apache Spark is an in-memory cluster based parallel processing system that provides a wide range of functionality like graph processing, machine learning, stream processing and SQL. It operates at unprecedented speeds, is easy to use and offers a rich set of data transformations. This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand Spark functionality. The book commences with an overview of the Spark eco-system. You will learn how to use MLlib to create a fully working neural net for handwriting recognition. You will then discover how stream processing can be tuned for optimal performance and to ensure parallel processing. The book extends to show how to incorporate H20 for machine learning, Titan for graph based storage, Databricks for cloud-based Spark. Intermediate Scala based code examples are provided for Apache Spark module processing in a CentOS Linux and Databricks cloud environment. ...
Data Algorithms
If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You'll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. ...
If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You'll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. ...
Practical Statistics for Data Scientists
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. Why exploratory data analysis is a key preliminary step in data science; How random sampling can reduce bias and yield a higher quality dataset, even with big data; How the principles of experimental design yield definitive answers to questions; How to use regression to estimate outcomes and detect anomalies; Key classification techni ...
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. Why exploratory data analysis is a key preliminary step in data science; How random sampling can reduce bias and yield a higher quality dataset, even with big data; How the principles of experimental design yield definitive answers to questions; How to use regression to estimate outcomes and detect anomalies; Key classification techni ...