Learning PySpark. Click here to enter text

Tomasz Drabas, Denny Lee

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.

SQL Server 2016 Developer's Guide. Build efficient database applications for your organization with SQL Server 2016

Milo?° Radivojevifá, Dejan Sarka, William Durkin

Microsoft SQL Server 2016 is considered the biggest leap in the data platform history of the Microsoft, in the ongoing era of Big Data and data science. This book introduces you to the new features of SQL Server 2016 that will open a completely new set of possibilities for you as a developer. It prepares you for the more advanced topics by starting with a quick introduction to SQL Server 2016's new features and a recapitulation of the possibilities you may have already explored with previous versions of SQL Server. The next part introduces you to small delights in the Transact-SQL language and then switches to a completely new technology inside SQL Server - JSON support. We also take a look at the Stretch database, security enhancements, and temporal tables.The last chapters concentrate on implementing advanced topics, including Query Store, column store indexes, and In-Memory OLTP. You will finally be introduced to R and learn how to use the R language with Transact-SQL for data exploration and analysis. By the end of this book, you will have the required information to design efficient, high-performance database applications without any hassle.

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.

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

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.

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.

Learn Ethical Hacking from Scratch. Your stepping stone to penetration testing

Zaid Sabih

This book starts with the basics of ethical hacking, how to practice hacking safely and legally, and how to install and interact with Kali Linux and the Linux terminal. You will explore network hacking, where you will see how to test the security of wired and wireless networks. You’ll also learn how to crack the password for any Wi-Fi network (whether it uses WEP, WPA, or WPA2) and spy on the connected devices.Moving on, you will discover how to gain access to remote computer systems using client-side and server-side attacks. You will also get the hang of post-exploitation techniques, including remotely controlling and interacting with the systems that you compromised. Towards the end of the book, you will be able to pick up web application hacking techniques. You'll see how to discover, exploit, and prevent a number of website vulnerabilities, such as XSS and SQL injections.The attacks covered are practical techniques that work against real systems and are purely for educational purposes. At the end of each section, you will learn how to detect, prevent, and secure systems from these attacks.

Hands-on Machine Learning with JavaScript. Solve complex computational web problems using machine learning

Burak Kanber

In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added advantage of machine learning research and support for JS libraries, JavaScript makes your browsers smarter than ever with the ability to learn patterns and reproduce them to become a part of innovative products and applications.Hands-on Machine Learning with JavaScript presents various avenues of machine learning in a practical and objective way, and helps implement them using the JavaScript language. Predicting behaviors, analyzing feelings, grouping data, and building neural models are some of the skills you will build from this book. You will learn how to train your machine learning models and work with different kinds of data. During this journey, you will come across use cases such as face detection, spam filtering, recommendation systems, character recognition, and more. Moreover, you will learn how to work with deep neural networks and guide your applications to gain insights from data.By the end of this book, you'll have gained hands-on knowledge on evaluating and implementing the right model, along with choosing from different JS libraries, such as NaturalNode, brain, harthur, classifier, and many more to design smarter applications.