R Statistics Cookbook. Over 100 recipes for performing complex statistical operations with R 3.5

Francisco Juretig

R is a popular programming language for developing statistical software. This book will be a useful guide to solving common and not-so-common challenges in statistics. With this book, you'll be equipped to confidently perform essential statistical procedures across your organization with the help of cutting-edge statistical tools.You'll start by implementing data modeling, data analysis, and machine learning to solve real-world problems. You'll then understand how to work with nonparametric methods, mixed effects models, and hidden Markov models. This book contains recipes that will guide you in performing univariate and multivariate hypothesis tests, several regression techniques, and using robust techniques to minimize the impact of outliers in data.You'll also learn how to use the caret package for performing machine learning in R. Furthermore, this book will help you understand how to interpret charts and plots to get insights for better decision making.By the end of this book, you will be able to apply your skills to statistical computations using R 3.5. You will also become well-versed with a wide array of statistical techniques in R that are extensively used in the data science industry.

Hands-On Penetration Testing with Kali NetHunter. Spy on and protect vulnerable ecosystems using the power of Kali Linux for pentesting on the go

Glen D. Singh, Sean-Philip Oriyano

Kali NetHunter is a version of the popular and powerful Kali Linux pentesting platform, designed to be installed on mobile devices. Hands-On Penetration Testing with Kali NetHunter will teach you the components of NetHunter and how to install the software. You’ll also learn about the different tools included and how to optimize and use a package, obtain desired results, perform tests, and make your environment more secure. Starting with an introduction to Kali NetHunter, you will delve into different phases of the pentesting process. This book will show you how to build your penetration testing environment and set up your lab. You will gain insight into gathering intellectual data, exploiting vulnerable areas, and gaining control over target systems. As you progress through the book, you will explore the NetHunter tools available for exploiting wired and wireless devices. You will work through new ways to deploy existing tools designed to reduce the chances of detection. In the concluding chapters, you will discover tips and best practices for integrating security hardening into your Android ecosystem. By the end of this book, you will have learned to successfully use a mobile penetration testing device based on Kali NetHunter and Android to accomplish the same tasks you would traditionally, but in a smaller and more mobile form factor.

Machine Learning Algorithms. A reference guide to popular algorithms for data science and machine learning

Giuseppe Bonaccorso

In this book, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms that are covered in this book are linear regression, logistic regression, SVM, naïve Bayes, k-means, random forest, TensorFlow and feature engineering.In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously.On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problem

Learning Apache Apex. Real-time streaming applications with Apex

Thomas Weise, Munagala V. Ramanath, David Yan, Kenneth Knowles

Apache Apex is a next-generation stream processing framework designed to operate on data at large scale, with minimum latency, maximum reliability, and strict correctness guarantees.Half of the book consists of Apex applications, showing you key aspects of data processing pipelines such as connectors for sources and sinks, and common data transformations. The other half of the book is evenly split into explaining the Apex framework, and tuning, testing, and scaling Apex applications.Much of our economic world depends on growing streams of data, such as social media feeds, financial records, data from mobile devices, sensors and machines (the Internet of Things - IoT). The projects in the book show how to process such streams to gain valuable, timely, and actionable insights. Traditional use cases, such as ETL, that currently consume a significant chunk of data engineering resources are also covered.The final chapter shows you future possibilities emerging in the streaming space, and how Apache Apex can contribute to it.

Mastering C++ Programming. Modern C++ 17 at your fingertips

Jeganathan Swaminathan

C++ ? ?has ? ?come ? ?a ? ?long ? ?way ? ?and ? ?has ? ?now ? ?been ? ?adopted ? ?in ? ?several ? ?contexts. Its ? ?key ? ?strengths ? ?are ? ?its ? ?software ? ?infrastructure ? ?and ? ?resource-constrained applications. ? ?The ?C++ ? ?17 ? ?release ? ?will ? ?change ? ?the ? ?way ? ?developers ? ?write code, ? ?and ? ?this ? ?book ? ?will ? ?help ?you ? ?master ? ?your ? ?developing ? ?skills ? ?with ? ?C++. With ? ?real-world, ? ?practical ? ?examples ? ?explaining ? ?each ? ?concept, ? ?the ? ?book ? ?will begin ? ?by ? ?introducing ? ?you ? ?to ? ?the ? ?latest ? ?features ? ?in ? ?C++ ? ?17. ? ?It ? ?encourages clean ? ?code ? ?practices ? ?in ? ?C++ ? ?in ? ?general, ? ?and ? ?demonstrates ? ?the ? ?GUI app-development ? ?options ? ?in ? ?C++. ? ?You’ll ? ?get ? ?tips ? ?on ? ?avoiding ? ?memory ? ?leaks using ? ?smart-pointers. ? ?Next, ? ?you’ll ? ?see ? ?how ? ?multi-threaded ?programming can ? ?help ? ?you ? ?achieve ? ?concurrency ? ?in ? ?your ? ?applications. Moving ? ?on, ? ?you’ll ? ?get ? ?an ? ?in-depth ? ?understanding ? ?of ? ?the ? ?C++ ? ?Standard Template ? ?Library. ? ?We ? ?show ? ?you ? ?the ? ?concepts ? ?of ? ?implementing ? ?TDD ? ?and BDD ? ?in ? ?your ? ?C++ ? ?programs, ? ?and ? ?explore ? ?template-based ? ?generic programming, ? ?giving ? ?you ? ?the ? ?expertise ? ?to ? ?build ? ?powerful ? ?applications. Finally, ? ?we’ll ? ?round ? ?up ? ?with ? ?debugging ? ?techniques ? ?and ? ?best ? ?practices.By ? ?the ? ?end ? ?of ? ?the ? ?book, ? ?you’ll ? ?have ? ?an ? ?in-depth ? ?understanding ? ?of ? ?the language ? ?and ? ?its ? ?various ? ?facets.

JavaScript Cloud Native Development Cookbook. Deliver serverless cloud-native solutions on AWS, Azure, and GCP

John Gilbert

Cloud-native development is a modern approach to building and running applications that leverages the merits of the cloud computing model. With cloud-native development, teams can deliver faster and in a more lean and agile manner as compared to traditional approaches. This recipe-based guide provides quick solutions for your cloud-native applications.Beginning with a brief introduction, JavaScript Cloud-Native Development Cookbook guides you in building and deploying serverless, event-driven, cloud-native microservices on AWS with Node.js. You'll then move on to the fundamental patterns of developing autonomous cloud-native services and understand the tools and techniques involved in creating globally scalable, highly available, and resilient cloud-native applications. The book also covers multi-regional deployments and leveraging the edge of the cloud to maximize responsiveness, resilience, and elasticity.In the latter chapters you'll explore techniques for building fully automated, continuous deployment pipelines and gain insights into polyglot cloud-native development on popular cloud platforms such as Azure and Google Cloud Platform (GCP). By the end of the book, you'll be able to apply these skills to build powerful cloud-native solutions.

Natural Language Processing and Computational Linguistics. A practical guide to text analysis with Python, Gensim, spaCy, and Keras

Bhargav Srinivasa-Desikan

Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data.This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy.You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning.This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.

Hands-On Artificial Intelligence for Search. Building intelligent applications and perform enterprise searches

Devangini Patel

With the emergence of big data and modern technologies, AI has acquired a lot of relevance in many domains. The increase in demand for automation has generated many applications for AI in fields such as robotics, predictive analytics, finance, and more.In this book, you will understand what artificial intelligence is. It explains in detail basic search methods: Depth-First Search (DFS), Breadth-First Search (BFS), and A* Search, which can be used to make intelligent decisions when the initial state, end state, and possible actions are known. Random solutions or greedy solutions can be found for such problems. But these are not optimal in either space or time and efficient approaches in time and space will be explored. We will also understand how to formulate a problem, which involves looking at it and identifying its initial state, goal state, and the actions that are possible in each state. We also need to understand the data structures involved while implementing these search algorithms as they form the basis of search exploration. Finally, we will look into what a heuristic is as this decides the quality of one sub-solution over another and helps you decide which step to take.