Professional and Applied Computing

Examine the latest technological advancements in building a scalable machine learning model with Big Data using R. This book shows you how to work with a machine learning algorithm and use it to build a ML model from raw data. All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. For every machine learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R. All the images are available in color and hi-res as part of the code download. This new paradigm of teaching machine learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in this book makes it easy for someone to connect the dots.. What You'll Learn • Use the model building process flow • Apply theoretical aspects of machine learning • Review industry-based cae studies • Understand ML algorithms using R • Build machine learning models using Apache Hadoop and Spark Who This Book is For Data scientists, data science professionals and researchers in academia who want to understand the nuances of machine learning approaches/algorithms along with ways to see them in practice using R. The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark.

Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time and resources. Python Machine Learning Case Studies takes you through the steps to improve business processes and determine the pivotal points that frame strategies. You'll see machine learning techniques that you can use to support your products and services. Moreover you'll learn the pros and cons of each of the machine learning concepts to help you decide which one best suits your needs. By taking a step-by-step approach to coding in Python you'll be able to understand the rationale behind model selection and decisions within the machine learning process. The book is equipped with practical examples along with code snippets to ensure that you understand the data science approach to solving real-world problems. What You Will Learn • Gain insights into machine learning concepts • Work on real-world applications of machine learning • Learn concepts of model selection and optimization • Get a hands-on overview of Python from a machine learning point of view Who This Book Is For Data scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts.

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn • Perform data science and analytics using statistics and the R programming language • Visualize and explore data, including working with large data sets found in big data • Build an R package • Test and check your code • Practice version control • Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language.

Get started with Julia for engineering and numerical computing, especially data science, machine learning, and scientific computing applications. This book explains how Julia provides the functionality, ease-of-use and intuitive syntax of R, Python, MATLAB, SAS, or Stata combined with the speed, capacity, and performance of C, C++, or Java. You'll learn the OOP principles required to get you started, then how to do basic mathematics with Julia. Other core functionality of Julia that you'll cover, includes working with complex numbers, rational and irrational numbers, rings, and fields. Beginning Julia Programming takes you beyond these basics to harness Julia's powerful features for mathematical functions in Julia, arrays for matrix operations, plotting, and more. Along the way, you also learn how to manage strings, write functions, work with control flows, and carry out I/O to implement and leverage the mathematics needed for your data science and analysis projects. "Julia walks like Python and runs like C". This phrase explains why Julia is quickly growing as the most favored option for data analytics and numerical computation. After reading and using this book, you'll have the essential knowledge and skills to build your first Julia-based application. What You'll Learn • Obtain core skills in Julia • Apply Julia in engineering and science applications • Work with mathematical functions in Julia • Use arrays, strings, functions, control flow, and I/O in Julia • Carry out plotting and display basic graphics Who This Book Is For Those who are new to Julia; experienced users may also find this helpful as a reference.

Discover the difference between making a robot move and making a robot think. Using Mindstorms EV3 and LeJOS-an open source project for Java Mindstorms projects-you'll learn how to create Artificial Intelligence (AI) for your bot. Your robot will learn how to problem solve, how to plan, and how to communicate. Along the way, you'll learn about classical AI algorithms for teaching hardware how to think; algorithms that you can then apply to your own robotic inspirations. If you've ever wanted to learn about robotic intelligence in a practical, playful way, Beginning Robotics Programming in Java with LEGO Mindstorms is for you. What you'll learn: • Build your first LEGO EV3 robot step-by-step • Install LeJOS and its firmware on Lego EV3 • Create and upload your first Java program into Lego EV3 • Work with Java programming for motors • Understand robotics behavior programming with sensors • Review common AI algorithms, such as DFS, BFS, and Dijkstra's AlgorithmWho this book is for: Students, teachers, and makers with basic Java programming experience who want to learn how to apply Artificial Intelligence to a practical robotic system.

A major limitation of conventional web sites is their unorganized and isolated contents, which is created mainly for human consumption. This limitation can be addressed by organizing and publishing data, using powerful formats that add structure and meaning to the content of web pages and link related data to one another. Computers can "understand" such data better, which can be useful for task automation. The web sites that provide semantics (meaning) to software agents form the Semantic Web, the Artificial Intelligence extension of the World Wide Web. In contrast to the conventional Web (the "Web of Documents"), the Semantic Web includes the "Web of Data", which connects "things" (representing real-world humans and objects) rather than documents meaningless to computers. Mastering Structured Data on the Semantic Web explains the practical aspects and the theory behind the Semantic Web and how structured data, such as HTML5 Microdata and JSON-LD, can be used to improve your site's performance on next-generation Search Engine Result Pages and be displayed on Google Knowledge Panels. You will learn how to represent arbitrary fields of human knowledge in a machine-interpretable form using the Resource Description Framework (RDF), the cornerstone of the Semantic Web. You will see how to store and manipulate RDF data in purpose-built graph databases such as triplestores and quadstores, that are exploited in Internet marketing, social media, and data mining, in the form of Big Data applications such as the Google Knowledge Graph, Wikidata, or Facebook's Social Graph. With the constantly increasing user expectations in web services and applications, Semantic Web standards gain more popularity. This book will familiarize you with the leading controlled vocabularies and ontologies and explain how to represent your own concepts. After learning the principles of Linked Data, the five-star deployment scheme, and the Open Data concept, you will be able to create and interlink five-star Linked Open Data, and merge your RDF graphs to the LOD Cloud. The book also covers the most important tools for generating, storing, extracting, and visualizing RDF data, including, but not limited to, Protégé, TopBraid Composer, Sindice, Apache Marmotta, Callimachus, and Tabulator. You will learn to implement Apache Jena and Sesame in popular IDEs such as Eclipse and NetBeans, and use these APIs for rapid Semantic Web application development. Mastering Structured Data on the Semantic Web demonstrates how to represent and connect structured data to reach a wider audience, encourage data reuse, and provide content that can be automatically processed with full certainty. As a result, your web contents will be integral parts of the next revolution of the Web.

Do you want to create data analysis reports without writing a line of code? This book introduces SAS Studio, a free data science web browser-based product for educational and non-commercial purposes. The power of SAS Studio comes from its visual point-and-click user interface that generates SAS code. It is easier to learn SAS Studio than to learn R and Python to accomplish data cleaning, statistics, and visualization tasks. The book includes a case study about analyzing the data required for predicting the results of presidential elections in the state of Maine for 2016 and 2020. In addition to the presidential elections, the book provides real-life examples including analyzing stocks, oil and gold prices, crime, marketing, and healthcare. You will see data science in action and how easy it is to perform complicated tasks and visualizations in SAS Studio. You will learn, step-by-step, how to do visualizations, including maps. In most cases, you will not need a line of code as you work with the SAS Studio graphical user interface. The book includes explanations of the code that SAS Studio generates automatically. You will learn how to edit this code to perform more complicated advanced tasks. The book introduces you to multiple SAS products such as SAS Viya, SAS Analytics, and SAS Visual Statistics. What You Will Learn Become familiar with SAS Studio IDE Understand essential visualizations Know the fundamental statistical analysis required in most data science and analytics reports Clean the most common data set problems Use linear progression for data prediction Write programs in SAS Get introduced to SAS-Viya, which is more potent than SAS studio Who This Book Is For A general audience of people who are new to data science, students, and data analysts and scientists who are experienced but new to SAS. No programming or in-depth statistics knowledge is needed.

Learn to program the Raspberry Pi Pico's dual ARM Cortex M0+ CPUs in Assembly Language. The Pico contains a customer System on a Chip (SoC) called the RP2040, making it the Foundation's first entry into the low-cost microcontroller market. The RP2040 contains a wealth of coprocessors for performing arithmetic as well as performing specialized I/O functionality. This book will show you how these CPUs work from a low level, easy-to-learn perspective. There are eight new Programmable I/O (PIO) coprocessors that have their own specialized Assembly Language supporting a wide variety of interface protocols. You'll explore these protocols and write programs or functions in Assembly Language and interface to all the various bundled hardware interfaces. Then go beyond working on your own board and projects to contribute to the official RP2040 SDK. Finally, you'll take your DIY hardware projects to the next level of performance and functionality with more advanced programming skills. What You'll Learn Read and understand the Assembly Language code that is part of the Pico's SDK Integrate Assembly Language and C code together into one program Interface to available options for DIY electronics and IoT projects Who This Book Is For Makers who have already worked with microcontrollers, such as the Arduino or Pico, programming in C or Python. Those interested in going deeper and learning how these devices work at a lower level, by learning Assembly Language.