Python Machine Learning By Example. The easiest way to get into machine learning

Yuxi (Hayden) Liu

Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.

Practical Big Data Analytics. Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R

Nataraj Dasgupta

Big Data analytics relates to the strategies used by organizations to collect, organize, and analyze large amounts of data to uncover valuable business insights that cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization’s data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages, and BI tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology and the practical reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB, and even learn how to write R code for neural networks.By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using the different tools and methods articulatedin this book.

Kotlin for Enterprise Applications using Java EE. Develop, test, and troubleshoot enterprise applications and microservices with Kotlin and Java EE

Raghavendra Rao K

Kotlin was developed with a view to solving programmers’ difficulties and operational challenges. This book guides you in making Kotlin and Java EE work in unison to build enterprise-grade applications. Together, they can be used to create services of any size with just a few lines of code and let you focus on the business logic.Kotlin for Enterprise Applications using Java EE begins with a brief tour of Kotlin and helps you understand what makes it a popular and reasonable choice of programming language for application development, followed by its incorporation in the Java EE platform. We will then learn how to build applications using the Java Persistence API (JPA) and Enterprise JavaBeans (EJB), as well as develop RESTful web services and MicroServices. As we work our way through the chapters, we’ll use various performance improvement and monitoring tools for your application and see how they optimize real-world applications. At each step along the way, we will see how easy it is to develop enterprise applications in Kotlin. By the end of this book, we will have learned design patterns and how to implement them using Kotlin.

Game Physics Cookbook. Discover over 100 easy-to-follow recipes to help you implement efficient game physics and collision detection in your games

Gabor Szauer

Physics is really important for game programmers who want to add realism and functionality to their games. Collision detection in particular is a problem that affects all game developers, regardless of the platform, engine, or toolkit they use.This book will teach you the concepts and formulas behind collision detection. You will also be taught how to build a simple physics engine, where Rigid Body physics is the main focus, and learn about intersection algorithms for primitive shapes.You’ll begin by building a strong foundation in mathematics that will be used throughout the book. We’ll guide you through implementing 2D and 3D primitives and show you how to perform effective collision tests for them. We then pivot to one of the harder areas of game development—collision detection and resolution. Further on, you will learn what a Physics engine is, how to set up a game window, and how to implement rendering. We’ll explore advanced physics topics such as constraint solving. You’ll also find out how to implement a rudimentary physics engine, which you can use to build an Angry Birds type of game or a more advanced game.By the end of the book, you will have implemented all primitive and some advanced collision tests, and you will be able to read on geometry and linear Algebra formulas to take forward to your own games!

Hands-On Networking with Azure. Build large-scale, real-world apps using Azure networking solutions

Mohamed Waly

Microsoft Azure networking is one of the most valuable and important offerings in Azure. No matter what solution you are building for the cloud, you'll fi nd a compelling use for it. This book will get you up to speed quickly on Microsoft Azure Networking by teaching you how to use different networking services.By reading this book, you will develop a strong networking foundation for Azure virtual machines and for expanding your on-premise environment to Azure. Hands-On Networking with Azure starts with an introduction to Microsoft Azure networking and creating Azure Virtual Networks with subnets of different types within them. The book helps you understand the architecture of Azure networks. You will then learn the best practices for designing both Windows- and Linux-based Azure VM networks. You will also learn to expand your networks into Azure and how to use Azure DNS. Moreover, you will master best practices for dealing with Azure Load Balancer and the solutions they offer in different scenarios. Finally, we will demonstrate how the Azure Application Gateway works, offering various layer-7 load balancing capabilities for applications. By the end of this book, you will be able to architect your networking solutions for Azure.

Learning Functional Programming in Go. Change the way you approach your applications using functional programming in Go

Lex Sheehan

Lex Sheehan begins slowly, using easy-to-understand illustrations and working Go code to teach core functional programming (FP) principles such as referential transparency, laziness, recursion, currying, and chaining continuations.This book is a tutorial for programmers looking to learn FP and apply it to write better code. Lex guides readers from basic techniques to advanced topics in a logical, concise, and clear progression.The book is divided into four modules. The first module explains the functional style of programming: pure functional programming, manipulating collections, and using higher-order functions. In the second module, you will learn design patterns that you can use to build FP-style applications. In the next module, you will learn FP techniques that you can use to improve your API signatures, increase performance, and build better cloud-native applications. The last module covers Category Theory, Functors, Monoids, Monads, Type classes and Generics.By the end of the book, you will be adept at building applications the FP way.

Blockchain Developer's Guide. Develop smart applications with Blockchain technologies - Ethereum, JavaScript, Hyperledger Fabric, and Corda

Brenn Hill, Samanyu Chopra, Paul Valencourt, Narayan Prusty

Blockchain applications provide a single-shared ledger to eliminate trust issues involving multiple stakeholders. It is the main technical innovation of Bitcoin, where it serves as the public ledger for Bitcoin transactions.Blockchain Developer's Guide takes you through the electrifying world of blockchain technology. It begins with the basic design of a blockchain and elaborates concepts, such as Initial Coin Offerings (ICOs), tokens, smart contracts, and other related terminologies. You will then explore the components of Ethereum, such as Ether tokens, transactions, and smart contracts that you need to build simple DApps.Blockchain Developer's Guide also explains why you must specifically use Solidity for Ethereum-based projects and lets you explore different blockchains with easy-to-follow examples. You will learn a wide range of concepts - beginning with cryptography in cryptocurrencies and including ether security, mining, and smart contracts. You will learn how to use web sockets and various API services for Ethereum. By the end of this Learning Path, you will be able to build efficient decentralized applications.This Learning Path includes content from the following Packt products:• Blockchain Quick Reference by Brenn Hill, Samanyu Chopra, Paul Valencourt• Building Blockchain Projects by Narayan Prusty

Hands-On Automated Machine Learning. A beginner's guide to building automated machine learning systems using AutoML and Python

Sibanjan Das, Umit Mert Cakmak

AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this book, you’ll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you’ll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions.