Practical Automated Machine Learning Using H2O.ai. Discover the power of automated machine learning, from experimentation through to deployment to production

Salil Ajgaonkar

With the huge amount of data being generated over the internet and the benefits that Machine Learning (ML) predictions bring to businesses, ML implementation has become a low-hanging fruit that everyone is striving for. The complex mathematics behind it, however, can be discouraging for a lot of users. This is where H2O comes in – it automates various repetitive steps, and this encapsulation helps developers focus on results rather than handling complexities.You’ll begin by understanding how H2O’s AutoML simplifies the implementation of ML by providing a simple, easy-to-use interface to train and use ML models. Next, you’ll see how AutoML automates the entire process of training multiple models, optimizing their hyperparameters, as well as explaining their performance. As you advance, you’ll find out how to leverage a Plain Old Java Object (POJO) and Model Object, Optimized (MOJO) to deploy your models to production. Throughout this book, you’ll take a hands-on approach to implementation using H2O that’ll enable you to set up your ML systems in no time.By the end of this H2O book, you’ll be able to train and use your ML models using H2O AutoML, right from experimentation all the way to production without a single need to understand complex statistics or data science.

Practical AWS Networking. Build and manage complex networks using services such as Amazon VPC, Elastic Load Balancing, Direct Connect, and Amazon Route 53

Mitesh Soni

Amazon Web Services (AWS) dominates the public cloud market by a huge margin and continues to be the first choice for many organizations. Networking has been an area of focus for all the leading cloud service providers. AWS has a suite of network-related products which help in performing network related task on AWS.This book initially covers the basics of networking in AWS. Then we use AWS VPC to create an isolated virtual cloud for performing network-related tasks. We then provide an overview of AWS Direct Connect after taking a deep dive into scalability and load balancing using the auto scaling feature, Elastic Load Balancing, and Amazon Route S3. Toward the end of the book, we cover troubleshooting tips and security best practices for your network. By the end of this book, you will have hands-on experience of working with network tasks on AWS.

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.

Practical Business Intelligence. Optimize Business Intelligence for Efficient Data Analysis

Ahmed Sherif

Business Intelligence (BI) is at the crux of revolutionizing enterprise. Everyone wants to minimize losses and maximize profits. Thanks to Big Data and improved methodologies to analyze data, Data Analysts and Data Scientists are increasingly using data to make informed decisions. Just knowing how to analyze data is not enough, you need to start thinking how to use data as a business asset and then perform the right analysis to build an insightful BI solution. Efficient BI strives to achieve the automation of data for ease of reporting and analysis. Through this book, you will develop the ability to think along the right lines and use more than one tool to perform analysis depending on the needs of your business. We start off by preparing you for data analytics. We then move on to teach you a range of techniques to fetch important information from various databases, which can be used to optimize your business.The book aims to provide a full end-to-end solution for an environment setup that can help you make informed business decisions and deliver efficient and automated BI solutions to any company.It is a complete guide for implementing Business intelligence with the help of the most powerful tools like D3.js, R, Tableau, Qlikview and Python that are available on the market.

Practical C++ Game Programming with Data Structures and Algorithms. Write high-performance code and solve game development challenges with expert-led C++ solutions

Zhenyu George Li, Charles Shih-I Yeh, Dr. E. Wyn Roberts

Game development demands performance-driven solutions—from rendering complex 3D scenes to managing AI behavior and handling animations. This hands-on book guides you in solving these challenges using high-performance C++ data structures and algorithms. The authors distill their decades of combined industry experience to help you apply practical C++ techniques to create smooth, scalable, and intelligent games. Instead of pseudocode or abstract theory, you’ll work with real, executable examples that translate directly to your projects. Demos in this book have been developed based on raylib library, providing accessible and efficient foundations for learning.Opening with a structured overview of algorithmic thinking in C++ game development, the book covers advanced algorithms and their applications in real-world game scenarios. You’ll implement data structures that optimize game functionality, solve algorithmic problems efficiently, and enhance gameplay with robust AI and rendering techniques. In addition, the key focus is collision detection, 2D/3D rendering, animation, camera controls, and machine learning-driven AI, all through practical, executable C++ code.By the end of this book, you’ll be able to confidently apply advanced C++ techniques to develop efficient, scalable, and feature-rich gameplay systems for modern games.

Practical C Programming. Solutions for modern C developers to create efficient and well-structured programs

B. M. Harwani

Used in everything from microcontrollers to operating systems, C is a popular programming language among developers because of its flexibility and versatility. This book helps you get hands-on with various tasks, covering the fundamental as well as complex C programming concepts that are essential for making real-life applications.You’ll start with recipes for arrays, strings, user-defined functions, and pre-processing directives. Once you’re familiar with the basic features, you’ll gradually move on to learning pointers, file handling, concurrency, networking, and inter-process communication (IPC). The book then illustrates how to carry out searching and arrange data using different sorting techniques, before demonstrating the implementation of data structures such as stacks and queues. Later, you’ll learn interesting programming features such as using graphics for drawing and animation, and the application of general-purpose utilities. Finally, the book will take you through advanced concepts such as low-level programming, embedded software, IoT, and security in coding, as well as techniques for improving code performance.By the end of this book, you'll have a clear understanding of C programming, and have the skills you need to develop robust apps.

Practical Change Management for IT Projects. Transform your IT project and make change stick with this step-by-step guide with this book and

Emily Carr

Transform your IT project and make change stick with this step-by-step guide.In today’s fast-paced world of change, companies expect you to do more, with less. Drawing on over a decade of Change Management experience as a consultant with Fortune 500 companies including IBM and NCR, Emily Carr shares the secrets to making change happen smoothly.If your company is like most, the number one reason that projects have failed over the years don’t have to do with technology. They have to do with people. People didn’t like the new technology. People weren’t trained properly on the change. People hadn’t received adequate communications and didn’t understand the change. Sound familiar?Project teams rarely forget to work on the technology, but they often forget to work with the people, and no matter how amazing your new technology is, it’s useless unless people use it efficiently.This book will help you focus on the people.Packed with templates, checklists, and real-life examples, this user-friendly guide will provide you with the insights and guidance of an expert consultant, for a fraction of the price. You’ll follow a clearly laid out path from Change Management novice to confident and prepared change manager. You’ll be introduced to the Five Pillars of Change: Sponsorship, Stakeholder Management, Communication, Training, and Organization Design. You will work step-by-step through templates in each pillar to build and run a comprehensive Change Management plan tailor-made to your project and organization.

Practical Cloud-Native Java Development with MicroProfile. Develop and deploy scalable, resilient, and reactive cloud-native applications using MicroProfile 4.1

Emily Jiang, Andrew McCright, John Alcorn, David Chan, ...

In this cloud-native era, most applications are deployed in a cloud environment that is public, private, or a combination of both. To ensure that your application performs well in the cloud, you need to build an application that is cloud native. MicroProfile is one of the most popular frameworks for building cloud-native applications, and fits well with Kubernetes. As an open standard technology, MicroProfile helps improve application portability across all of MicroProfile's implementations.Practical Cloud-Native Java Development with MicroProfile is a comprehensive guide that helps you explore the advanced features and use cases of a variety of Jakarta and MicroProfile specifications. You'll start by learning how to develop a real-world stock trader application, and then move on to enhancing the application and adding day-2 operation considerations. You'll gradually advance to packaging and deploying the application. The book demonstrates the complete process of development through to deployment and concludes by showing you how to monitor the application's performance in the cloud. By the end of this book, you will master MicroProfile's latest features and be able to build fast and efficient cloud-native applications.

Practical Computer Vision. Extract insightful information from images using TensorFlow, Keras, and OpenCV

Abhinav Dadhich

In this book, you will find several recently proposed methods in various domains of computer vision. You will start by setting up the proper Python environment to work on practical applications. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. Using these libraries, you'll start to understand the concepts of image transformation and filtering. You will find a detailed explanation of feature detectors such as FAST and ORB; you'll use them to find similar-looking objects.With an introduction to convolutional neural nets, you will learn how to build a deep neural net using Keras and how to use it to classify the Fashion-MNIST dataset. With regard to object detection, you will learn the implementation of a simple face detector as well as the workings of complex deep-learning-based object detectors such as Faster R-CNN and SSD using TensorFlow. You'll get started with semantic segmentation using FCN models and track objects with Deep SORT. Not only this, you will also use Visual SLAM techniques such as ORB-SLAM on a standard dataset. By the end of this book, you will have a firm understanding of the different computer vision techniques and how to apply them in your applications.

Practical Convolutional Neural Networks. Implement advanced deep learning models using Python

Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari

Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available.Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.

Practical Cybersecurity Architecture. A guide to creating and implementing robust designs for cybersecurity architects

Ed Moyle, Diana Kelley

Cybersecurity architects work with others to develop a comprehensive understanding of the business' requirements. They work with stakeholders to plan designs that are implementable, goal-based, and in keeping with the governance strategy of the organization.With this book, you'll explore the fundamentals of cybersecurity architecture: addressing and mitigating risks, designing secure solutions, and communicating with others about security designs. The book outlines strategies that will help you work with execution teams to make your vision a concrete reality, along with covering ways to keep designs relevant over time through ongoing monitoring, maintenance, and continuous improvement. As you progress, you'll also learn about recognized frameworks for building robust designs as well as strategies that you can adopt to create your own designs.By the end of this book, you will have the skills you need to be able to architect solutions with robust security components for your organization, whether they are infrastructure solutions, application solutions, or others.

Practical Cybersecurity Architecture. A guide to creating and implementing robust designs for cybersecurity architects - Second Edition

Diana Kelley, Ed Moyle

Cybersecurity architecture is the discipline of systematically ensuring that an organization is resilient against cybersecurity threats. Cybersecurity architects work in tandem with stakeholders to create a vision for security in the organization and create designs that are implementable, goal-based, and aligned with the organization’s governance strategy.Within this book, you'll learn the fundamentals of cybersecurity architecture as a practical discipline. These fundamentals are evergreen approaches that, once mastered, can be applied and adapted to new and emerging technologies like artificial intelligence and machine learning. You’ll learn how to address and mitigate risks, design secure solutions in a purposeful and repeatable way, communicate with others about security designs, and bring designs to fruition. This new edition outlines strategies to help you work with execution teams to make your vision a reality, along with ways of keeping designs relevant over time. As you progress, you'll also learn about well-known frameworks for building robust designs and strategies that you can adopt to create your own designs.By the end of this book, you’ll have the foundational skills required to build infrastructure, cloud, AI, and application solutions for today and well into the future with robust security components for your organization.

Practical Data Analysis Cookbook. Over 60 practical recipes on data exploration and analysis

Tomasz Drabas

Data analysis is the process of systematically applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data. Its importance has been most visible in the sector of information and communication technologies. It is an employee asset in almost all economy sectors.This book provides a rich set of independent recipes that dive into the world of data analytics and modeling using a variety of approaches, tools, and algorithms. You will learn the basics of data handling and modeling, and will build your skills gradually toward more advanced topics such as simulations, raw text processing, social interactions analysis, and more.First, you will learn some easy-to-follow practical techniques on how to read, write, clean, reformat, explore, and understand your data—arguably the most time-consuming (and the most important) tasks for any data scientist. In the second section, different independent recipes delve into intermediate topics such as classification, clustering, predicting, and more. With the help of these easy-to-follow recipes, you will also learn techniques that can easily be expanded to solve other real-life problems such as building recommendation engines or predictive models. In the third section, you will explore more advanced topics: from the field of graph theory through natural language processing, discrete choice modeling to simulations. You will also get to expand your knowledge on identifying fraud origin with the help of a graph, scrape Internet websites, and classify movies based on their reviews.By the end of this book, you will be able to efficiently use the vast array of tools that the Python environment has to offer.

Practical Data Analysis. For small businesses, analyzing the information contained in their data using open source technology could be game-changing. All you need is some basic programming and mathematical skills to do just that

Hector Cuesta

Plenty of small businesses face big amounts of data but lack the internal skills to support quantitative analysis. Understanding how to harness the power of data analysis using the latest open source technology can lead them to providing better customer service, the visualization of customer needs, or even the ability to obtain fresh insights about the performance of previous products. Practical Data Analysis is a book ideal for home and small business users who want to slice and dice the data they have on hand with minimum hassle.Practical Data Analysis is a hands-on guide to understanding the nature of your data and turn it into insight. It will introduce you to the use of machine learning techniques, social networks analytics, and econometrics to help your clients get insights about the pool of data they have at hand. Performing data preparation and processing over several kinds of data such as text, images, graphs, documents, and time series will also be covered.Practical Data Analysis presents a detailed exploration of the current work in data analysis through self-contained projects. First you will explore the basics of data preparation and transformation through OpenRefine. Then you will get started with exploratory data analysis using the D3js visualization framework. You will also be introduced to some of the machine learning techniques such as, classification, regression, and clusterization through practical projects such as spam classification, predicting gold prices, and finding clusters in your Facebook friends' network. You will learn how to solve problems in text classification, simulation, time series forecast, social media, and MapReduce through detailed projects. Finally you will work with large amounts of Twitter data using MapReduce to perform a sentiment analysis implemented in Python and MongoDB. Practical Data Analysis contains a combination of carefully selected algorithms and data scrubbing that enables you to turn your data into insight.

Practical Data Analysis. Pandas, MongoDB, Apache Spark, and more - Second Edition

Hector Cuesta, Dr. Sampath Kumar

Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service.This book explains the basic data algorithms without the theoretical jargon, and you’ll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark.

Practical Data Analysis Using Jupyter Notebook. Learn how to speak the language of data by extracting useful and actionable insights using Python

Marc Wintjen, Andrew Vlahutin

Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data.After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps.Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries.By the end of this book, you'll have gained the practical skills you need to analyze data with confidence.