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Practical Linux DevOps: Building a Linux Lab for Modern Software Development
by John S. TonelloLearn, develop and hone your Linux and DevOps skills by building a lab for learning, testing and exploring the latest Linux and open-source technologies. This book helps Linux users and others to master modern DevOps practices using a wide range of software and tools.Having a home or work-based Linux lab is indispensable to anyone looking to experiment with the ever-evolving landscape of new software and DevOps. With open-source tools and readily available hardware, you will end up with a lab you can use to try virtually any modern software, including Chef, Docker, Kubernetes and stalwarts like DNS, Dovecot, and Postfix for email. You'll set up pipelines for software deployment and focus on discrete projects that help you learn through doing. In the end, you'll acquire the skills needed to become better informed, more marketable engineers and developers, and better able to take on a wide array of software projects with confidence.Practical Linux DevOps is the perfect companion for those who want to learn how to build systems with utility and learn about modern hardware and software practices.What You'll LearnSet up a Linux-based virtualization environment and workstationCreate a lab network with a fully qualified domainBuild web-based applications with NGINX and LAMPUse version-control tools like GitAutomate deployments and configurationsThink like a modern DevOps engineerWho This Book Is ForNew and modestly experienced users with basic understanding of a basic understanding of Windows or Linux command line, as well as would-be and current DevOps engineers, and full-stack and other software developers
Practical Linux Forensics: A Guide for Digital Investigators
by Bruce NikkelA resource to help forensic investigators locate, analyze, and understand digital evidence found on modern Linux systems after a crime, security incident or cyber attack.Practical Linux Forensics dives into the technical details of analyzing postmortem forensic images of Linux systems which have been misused, abused, or the target of malicious attacks. It helps forensic investigators locate and analyze digital evidence found on Linux desktops, servers, and IoT devices. Throughout the book, you learn how to identify digital artifacts which may be of interest to an investigation, draw logical conclusions, and reconstruct past activity from incidents. You&’ll learn how Linux works from a digital forensics and investigation perspective, and how to interpret evidence from Linux environments. The techniques shown are intended to be independent of the forensic analysis platforms and tools used.Learn how to:Extract evidence from storage devices and analyze partition tables, volume managers, popular Linux filesystems (Ext4, Btrfs, and Xfs), and encryptionInvestigate evidence from Linux logs, including traditional syslog, the systemd journal, kernel and audit logs, and logs from daemons and applicationsReconstruct the Linux startup process, from boot loaders (UEFI and Grub) and kernel initialization, to systemd unit files and targets leading up to a graphical loginPerform analysis of power, temperature, and the physical environment of a Linux machine, and find evidence of sleep, hibernation, shutdowns, reboots, and crashesExamine installed software, including distro installers, package formats, and package management systems from Debian, Fedora, SUSE, Arch, and other distrosPerform analysis of time and Locale settings, internationalization including language and keyboard settings, and geolocation on a Linux systemReconstruct user login sessions (shell, X11 and Wayland), desktops (Gnome, KDE, and others) and analyze keyrings, wallets, trash cans, clipboards, thumbnails, recent files and other desktop artifactsAnalyze network configuration, including interfaces, addresses, network managers, DNS, wireless artifacts (Wi-Fi, Bluetooth, WWAN), VPNs (including WireGuard), firewalls, and proxy settingsIdentify traces of attached peripheral devices (PCI, USB, Thunderbolt, Bluetooth) including external storage, cameras, and mobiles, and reconstruct printing and scanning activity
Practical Linux Infrastructure
by Syed AliPractical Linux Infrastructure teaches you how to use the best open source tools to build a new Linux infrastructure, or alter an existing infrastructure, to ensure it stands up to enterprise-level needs. Each chapter covers a key area of implementation, with clear examples and step-by-step instructions. Using this book, you'll understand why scale matters, and what considerations you need to make. You'll see how to switch to using Google Cloud Platform for your hosted solution, how to use KVM for your virtualization, how to use Git, Postfix, and MySQL for your version control, email, and database, and how to use Puppet for your configuration management. For enterprise-level fault tolerance you'll use Apache, and for load balancing and high availability, you'll use HAProxy and Keepalived. For trend analysis you'll learn how to use Cacti, and for notification you'll use Nagios. You'll also learn how to utilize BIND to implement DNS, how to use DHCP (Dynamic Host Configuration Protocol), and how to setup remote access for your infrastructure using VPN and Iptables. You will finish by looking at the various tools you will need to troubleshoot issues that may occur with your hosted infrastructure. This includes how to use CPU, network, disk and memory management tools such as top, netstat, iostat and vmstat. Author Syed Ali is a senior site reliability engineering manager, who has extensive experience with virtualization and Linux cloud based infrastructure. His previous experience as an entrepreneur in infrastructure computing offers him deep insight into how a business can leverage the power of Linux to their advantage. He brings his expert knowledge to this book to teach others how to perfect their Linux environments. Become a Linux infrastructure pro with Practical Linux Infrastructure today.
Practical Linux Security Cookbook
by Tajinder KalsiSecure your Linux machines and keep them secured with the help of exciting recipes About This Book * This book provides code-intensive discussions with detailed recipes that help you understand better and learn faster. * More than 50 hands-on recipes to create and administer a secure Linux system locally as well as on a network * Enhance file system security and local and remote user authentication by using various security tools and different versions of Linux for different tasks Who This Book Is For Practical Linux Security Cookbook is intended for all those Linux users who already have knowledge of Linux File systems and administration. You should be familiar with basic Linux commands. Understanding Information security and its risks to a Linux system is also helpful in understanding the recipes more easily. However, even if you are unfamiliar with Information security, you will be able to easily follow and understand the recipes discussed. Since Linux Security Cookbook follows a practical approach, following the steps is very easy. What You Will Learn * Learn about various vulnerabilities and exploits in relation to Linux systems * Configure and build a secure kernel and test it * Learn about file permissions and security and how to securely modify files * Explore various ways to authenticate local users while monitoring their activities. * Authenticate users remotely and securely copy files on remote systems * Review various network security methods including firewalls using iptables and TCP Wrapper * Explore various security tools including Port Sentry, Squid Proxy, Shorewall, and many more * Understand Bash vulnerability/security and patch management In Detail With the growing popularity of Linux, more and more administrators have started moving to the system to create networks or servers for any task. This also makes Linux the first choice for any attacker now. Due to the lack of information about security-related attacks, administrators now face issues in dealing with these attackers as quickly as possible. Learning about the different types of Linux security will help create a more secure Linux system. Whether you are new to Linux administration or experienced, this book will provide you with the skills to make systems more secure. With lots of step-by-step recipes, the book starts by introducing you to various threats to Linux systems. You then get to walk through customizing the Linux kernel and securing local files. Next you will move on to manage user authentication locally and remotely and also mitigate network attacks. Finally, you will learn to patch bash vulnerability and monitor system logs for security. With several screenshots in each example, the book will supply a great learning experience and help you create more secure Linux systems. Style and approach An easy-to-follow cookbook with step-by-step practical recipes covering the various Linux security administration tasks. Each recipe has screenshots, wherever needed, to make understanding more easy.
Practical Linux Security Cookbook: Secure your Linux environment from modern-day attacks with practical recipes, 2nd Edition
by Tajinder KalsiEnhance file system security and learn about network attack, security tools and different versions of Linux build.Key FeaturesHands-on recipes to create and administer a secure Linux systemEnhance file system security and local and remote user authenticationUse various security tools and different versions of Linux for different tasksBook DescriptionOver the last few years, system security has gained a lot of momentum and software professionals are focusing heavily on it. Linux is often treated as a highly secure operating system. However, the reality is that Linux has its share of security flaws, and these security flaws allow attackers to get into your system and modify or even destroy your important data. But there’s no need to panic, since there are various mechanisms by which these flaws can be removed, and this book will help you learn about different types of Linux security to create a more secure Linux system. With a step-by-step recipe approach, the book starts by introducing you to various threats to Linux systems. Then, this book will walk you through customizing the Linux kernel and securing local files. Next, you will move on to managing user authentication both locally and remotely and mitigating network attacks. Later, you will learn about application security and kernel vulnerabilities. You will also learn about patching Bash vulnerability, packet filtering, handling incidents, and monitoring system logs. Finally, you will learn about auditing using system services and performing vulnerability scanning on Linux.By the end of this book, you will be able to secure your Linux systems and create a robust environment.What you will learnLearn about vulnerabilities and exploits in relation to Linux systemsConfigure and build a secure kernel and test itLearn about file permissions and how to securely modify filesAuthenticate users remotely and securely copy files on remote systemsReview different network security methods and toolsPerform vulnerability scanning on Linux machines using toolsLearn about malware scanning and read through logsWho this book is forThis book is intended for all those Linux users who already have knowledge of Linux file systems and administration. You should be familiar with basic Linux commands. Understanding information security and its risks to a Linux system is also helpful in understanding the recipes more easily.
Practical Linux System Administration: A Guide to Installation, Configuration, and Management
by Kenneth HessThis essential guide covers all aspects of Linux system administration, from user maintenance, backups, filesystem housekeeping, storage management, and network setup to hardware and software troubleshooting and some application management. It's both a practical daily reference manual for sysadmins and IT pros and a handy study guide for those taking Linux certification exams.You'll turn to it frequently, not only because of the sheer volume of valuable information it provides but because of the real-world examples within and the clear, useful way the information is presented. With this book at your side, you'll be able to:Install Linux and perform initial setup duties, such as connecting to a networkNavigate the Linux filesystem via the command lineInstall software from repositories and source and satisfy dependenciesSet permissions on files and directoriesCreate, modify, and remove user accountsSet up networkingFormat and mount filesystemsPerform basic troubleshooting on hardware and softwareCreate and manage logical volumesWork with SELinuxManage a firewall and iptablesShut down, reboot, and recover a systemPerform backups and restores
Practical Linux Topics
by Chris BinnieTeaches you how to improve your hands-on knowledge of Linux using challenging, real-world scenarios. Each chapter explores a topic that has been chosen specifically to demonstrate how to enhance your base Linux system, and resolve important issues. This book enables sysadmins, DevOps engineers, developers, and other technical professionals to make full use of Linux's rocksteady foundation. Explore specific topics in networking, email, filesystems, encryption, system monitoring, security, servers, and more-- including systemd and GPG. Understand salient security concerns and how to mitigate them. Applicable to almost all Linux flavors--Debian, Red Hat, Ubuntu, Linux Mint, CentOS--Practical Linux Topics �__c�__an be used to reference other Unix-type systems with little modification. Improve your practical know-how and background knowledge on servers and workstations alike, increase your ability to troubleshoot and ultimately solve the daily challenges encountered by all professional Linux users. Empower your Linux skills by adding Practical Linux Topics to your library today. What you'll learn Solve a variety of challenges faced by sysadmins and DevOps engineers Understand the security implications of the actions you take Study the history behind some of the packages that you are using for a greater in-depth understanding Become a professional at troubleshooting Extend your knowledge by learning about multiple OSs and third-party packages Who this book is for Having mastered the basics of running Linux systems this book takes you one step further to help you master the elements of Linux which you may have struggled with in the past. You have progressed past the basic stages of using Linux and want to delve into the more complex aspects. Practical Linux instantly offers answers to problematic scenarios and provides invaluable information for future reference. It is an invaluable addition to any Linux library. Table of Contents Chapter 1: Real-time Network Monitoring from the Console Chapter 2: Destroying Sensitive Data Chapter 3: Supercharged systemd Chapter 4: Zero Downtime Linux Chapter 5: Get More with wget Chapter 6: Securing SSH withPAM Chapter 7: Your Discs, Your Way Chapter 8: No SFTP Doesn't Mean No Encryption Chapter 9: Making the Most of the Screen Utility Chapter 10: Improve Your Security with SELinux Chapter 11: Nattier Networks Chapter 12: Keeping information Private with GPG Chapter 13: Get Permission From Your Peers with Sudo Chapter 14: Loop Disks and Wubi Appendix A: Hack, Break, and Fix Your Servers
Practical Linux with Raspberry Pi OS: Quick Start
by Ashwin PajankarQuickly start programming with Linux while learning the Raspberry Pi OS—the Linux distribution designed specifically for low-cost Raspberry Pis. This short guide reviews Linux commands, GUI, and shell scripting in a holistic manner by diving into both advanced and day-to-day tasks using the Raspberry Pi OS.You'll comfortably work with the Linux command prompt, and explore the RPi OS GUI and all its base applications. Then move into writing your own programs with shell-programming and using high-level languages such as C, C++, and Python 3. You’ll also study hardware and GPIO programming. Use Python 3 for GPIO programming to drive LEDs and pushbuttons.Examples are written in Shell, C, C++, and Python 3. Graphical output is displayed in helpful screenshots that capture just what you’ll see when working in this environment. All code examples are well tested on actual Raspberry Pi boards. After reading this book and following the examples, you’ll be able to write programs for demonstration in your academic/industrial research work, business environment, or just your circle of friends for fun! What You'll LearnNavigate the core aspects of Linux and programming on a Linux platform Install Raspberry Pi OS on a Raspberry PiProgram in Shell, C, C++, and PythonRedirect Io and work with the crontabWho This Book Is ForLinux enthusiasts, software engineers, researchers, business analysts, and managers working with the low-cost Raspberry Pi.
Practical MATLAB Deep Learning: A Project-Based Approach
by Stephanie Thomas Michael PaluszekHarness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. You’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Along the way, you'll learn to model complex systems, including the stock market, natural language, and angles-only orbit determination. You’ll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll also apply deep learning to aircraft navigation using images. Finally, you'll carry out classification of ballet pirouettes using an inertial measurement unit to experiment with MATLAB's hardware capabilities. What You Will LearnExplore deep learning using MATLAB and compare it to algorithmsWrite a deep learning function in MATLAB and train it with examplesUse MATLAB toolboxes related to deep learningImplement tokamak disruption predictionWho This Book Is For Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.
Practical MATLAB Deep Learning: A Projects-Based Approach
by Stephanie Thomas Michael Paluszek Eric HamHarness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. In this book, you’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. This edition includes new and expanded projects, and covers generative deep learning and reinforcement learning. Over the course of the book, you'll learn to model complex systems and apply deep learning to problems in those areas. Applications include: Aircraft navigationAn aircraft that lands on Titan, the moon of Saturn, using reinforcement learningStock market predictionNatural language processingMusic creation usng generative deep learningPlasma controlEarth sensor processing for spacecraftMATLAB Bluetooth data acquisition applied to dance physics What You Will LearnExplore deep learning using MATLAB and compare it to algorithmsWrite a deep learning function in MATLAB and train it with examplesUse MATLAB toolboxes related to deep learningImplement tokamak disruption predictionNow includes reinforcement learningWho This Book Is For Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.
Practical MATLAB Modeling with Simulink: Programming and Simulating Ordinary and Partial Differential Equations
by Sulaymon L. EshkabilovEmploy the essential and hands-on tools and functions of MATLAB's ordinary differential equation (ODE) and partial differential equation (PDE) packages, which are explained and demonstrated via interactive examples and case studies. This book contains dozens of simulations and solved problems via m-files/scripts and Simulink models which help you to learn programming and modeling of more difficult, complex problems that involve the use of ODEs and PDEs.You’ll become efficient with many of the built-in tools and functions of MATLAB/Simulink while solving more complex engineering and scientific computing problems that require and use differential equations. Practical MATLAB Modeling with Simulink explains various practical issues of programming and modelling.After reading and using this book, you'll be proficient at using MATLAB and applying the source code from the book's examples as templates for your own projects in data science or engineering. What You Will LearnModel complex problems using MATLAB and SimulinkGain the programming and modeling essentials of MATLAB using ODEs and PDEsUse numerical methods to solve 1st and 2nd order ODEsSolve stiff, higher order, coupled, and implicit ODEsEmploy numerical methods to solve 1st and 2nd order linear PDEsSolve stiff, higher order, coupled, and implicit PDEsWho This Book Is ForEngineers, programmers, data scientists, and students majoring in engineering, applied/industrial math, data science, and scientific computing. This book continues where Apress' Beginning MATLAB and Simulink leaves off.
Practical MATLAB: With Modeling, Simulation, and Processing Projects
by Irfan TurkApply MATLAB programming to the mathematical modeling of real-life problems from a wide range of topics. This pragmatic book shows you how to solve your programming problems, starting with a brief primer on MATLAB and the fundamentals of the MATLAB programming language. Then, you’ll build fully working examples and computational models found in the financial, engineering, and scientific sectors. As part of this section, you’ll cover signal and image processing, as well as GUIs. After reading and using Practical MATLAB and its accompanying source code, you’ll have the practical know-how and code to apply to your own MATLAB programming projects. What You Will LearnDiscover the fundamentals of MATLAB and how to get started with it for problem solvingApply MATLAB to a variety of problems and case studiesCarry out economic and financial modeling with MATLAB, including option pricing and compound interestUse MATLAB for simulation problems such as coin flips, dice rolling, random walks, and traffic flowsSolve computational biology problems with MATLABImplement signal processing with MATLAB, including currents, Fast Fourier Transforms (FFTs), and harmonic analysisProcess images with filters and edge detectionBuild applications with GUIs Who This Book Is ForPeople with some prior experience with programming and MATLAB.
Practical MLOps: Operationalizing Machine Learning Models
by Noah Gift Alfredo DezaGetting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.You'll discover how to:Apply DevOps best practices to machine learningBuild production machine learning systems and maintain themMonitor, instrument, load-test, and operationalize machine learning systemsChoose the correct MLOps tools for a given machine learning taskRun machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware
Practical Machine Learning
by Sunila GollapudiThis book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Some knowledge of Java programming and any scripting language is advisable if you want to get started immediately.
Practical Machine Learning Cookbook
by Atul TripathiResolving and offering solutions to your machine learning problems with R About This Book • Implement a wide range of algorithms and techniques for tackling complex data • Improve predictions and recommendations to have better levels of accuracy • Optimize performance of your machine-learning systems Who This Book Is For This book is for analysts, statisticians, and data scientists with knowledge of fundamentals of machine learning and statistics, who need help in dealing with challenging scenarios faced every day of working in the field of machine learning and improving system performance and accuracy. It is assumed that as a reader you have a good understanding of mathematics. Working knowledge of R is expected. What You Will Learn • Get equipped with a deeper understanding of how to apply machine-learning techniques • Implement each of the advanced machine-learning techniques • Solve real-life problems that are encountered in order to make your applications produce improved results • Gain hands-on experience in problem solving for your machine-learning systems • Understand the methods of collecting data, preparing data for usage, training the model, evaluating the model's performance, and improving the model's performance In Detail Machine learning has become the new black. The challenge in today's world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations. The first half of the book provides recipes on fairly complex machine-learning systems, where you'll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more. The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one. Style and approach Following a cookbook approach, we'll teach you how to solve everyday difficulties and struggles you encounter.
Practical Machine Learning Illustrated with KNIME
by Qin Li Geng Yang Yu Geng Wan QiuThis book guides professionals and students from various backgrounds to use machine learning in their own fields with low-code platform KNIME and without coding. Many people from various industries need use machine learning to solve problems in their own domains. However, machine learning is often viewed as the domain of programmers, especially for those who are familiar with Python. It is too hard for people from different backgrounds to learn Python to use machine learning. KNIME, the low-code platform, comes to help. KNIME helps people use machine learning in an intuitive environment, enabling everyone to focus on what to do instead of how to do. This book helps the readers gain an intuitive understanding of the basic concepts of machine learning through illustrations to practice machine learning in their respective fields. The author provides a practical guide on how to participate in Kaggle completions with KNIME to practice machine learning techniques.
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python
by Himanshu SinghGain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application.What You Will LearnDiscover image-processing algorithms and their applications using PythonExplore image processing using the OpenCV libraryUse TensorFlow, scikit-learn, NumPy, and other librariesWork with machine learning and deep learning algorithms for image processingApply image-processing techniques to five real-time projectsWho This Book Is ForData scientists and software developers interested in image processing and computer vision.
Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images
by Valliappa Lakshmanan Martin Görner Ryan GillardThis practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability.Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.You'll learn how to:Design ML architecture for computer vision tasksSelect a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your taskCreate an end-to-end ML pipeline to train, evaluate, deploy, and explain your modelPreprocess images for data augmentation and to support learnabilityIncorporate explainability and responsible AI best practicesDeploy image models as web services or on edge devicesMonitor and manage ML models
Practical Machine Learning for Streaming Data with Python: Design, Develop, and Validate Online Learning Models
by Sayan PutatundaDesign, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights. You'll start with an introduction to streaming data, the various challenges associated with it, some of its real-world business applications, and various windowing techniques. You'll then examine incremental and online learning algorithms, and the concept of model evaluation with streaming data and get introduced to the Scikit-Multiflow framework in Python. This is followed by a review of the various change detection/concept drift detection algorithms and the implementation of various datasets using Scikit-Multiflow.Introduction to the various supervised and unsupervised algorithms for streaming data, and their implementation on various datasets using Python are also covered. The book concludes by briefly covering other open-source tools available for streaming data such as Spark, MOA (Massive Online Analysis), Kafka, and more.What You'll LearnUnderstand machine learning with streaming data conceptsReview incremental and online learningDevelop models for detecting concept driftExplore techniques for classification, regression, and ensemble learning in streaming data contextsApply best practices for debugging and validating machine learning models in streaming data contextGet introduced to other open-source frameworks for handling streaming data.Who This Book Is ForMachine learning engineers and data science professionals
Practical Machine Learning in JavaScript: TensorFlow.js for Web Developers
by Charlie GerardBuild machine learning web applications without having to learn a new language. This book will help you develop basic knowledge of machine learning concepts and applications. You’ll learn not only theory, but also dive into code samples and example projects with TensorFlow.js. Using these skills and your knowledge as a web developer, you’ll add a whole new field of development to your tool set. This will give you a more concrete understanding of the possibilities offered by machine learning. Discover how ML will impact the future of not just programming in general, but web development specifically. Machine learning is currently one of the most exciting technology fields with the potential to impact industries from health to home automation to retail, and even art. Google has now introduced TensorFlow.js—an iteration of TensorFlow aimed directly at web developers. Practical Machine Learning in JavaScript will help you stay relevant in the tech industry with new tools, trends, and best practices.What You'll LearnUse the JavaScript framework for MLBuild machine learning applications for the webDevelop dynamic and intelligent web contentWho This Book Is ForWeb developers and who want a hands-on introduction to machine learning in JavaScript. A working knowledge of the JavaScript language is recommended.
Practical Machine Learning in R
by Mike Chapple Fred NwangangaGuides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning—a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions—allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more. Explores data management techniques, including data collection, exploration and dimensionality reduction Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.
Practical Machine Learning with AWS: Process, Build, Deploy, and Productionize Your Models Using AWS
by Himanshu SinghSuccessfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load Balancer, and Cloud Formation. It also covers cloud security topics such as AWS Compliance and artifacts, and the AWS Shield and CloudWatch monitoring service built for developers and DevOps engineers. Part II covers machine learning in AWS using SageMaker, which gives developers and data scientists the ability to build, train, and deploy machine learning models. Part III explores other AWS services such as Amazon Comprehend (a natural language processing service that uses machine learning to find insights and relationships in text), Amazon Forecast (helps you deliver accurate forecasts), and Amazon Textract.By the end of the book, you will understand the machine learning pipeline and how to execute any machine learning model using AWS. The book will also help you prepare for the AWS Certified Machine Learning—Specialty certification exam.What You Will LearnBe familiar with the different machine learning services offered by AWS Understand S3, EC2, Identity Access Management, and Cloud FormationUnderstand SageMaker, Amazon Comprehend, and Amazon ForecastExecute live projects: from the pre-processing phase to deployment on AWSWho This Book Is ForMachine learning engineers who want to learn AWS machine learning services, and acquire an AWS machine learning specialty certification
Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI
by Darren CookMachine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that’s easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.If you’re familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets. You’ll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning.Learn how to import, manipulate, and export data with H2OExplore key machine-learning concepts, such as cross-validation and validation data setsWork with three diverse data sets, including a regression, a multinomial classification, and a binomial classificationUse H2O to analyze each sample data set with four supervised machine-learning algorithmsUnderstand how cluster analysis and other unsupervised machine-learning algorithms work
Practical Machine Learning with R: Define, build, and evaluate machine learning models for real-world applications
by Monicah Wambugu Brindha Priyadarshini Jeyaraman Ludvig Renbo OlsenUnderstand how machine learning works and get hands-on experience of using R to build algorithms that can solve various real-world problems Key Features Gain a comprehensive overview of different machine learning techniques Explore various methods for selecting a particular algorithm Implement a machine learning project from problem definition through to the final model Book Description With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way. Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you'll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you'll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it. What you will learn Define a problem that can be solved by training a machine learning model Obtain, verify and clean data before transforming it into the correct format for use Perform exploratory analysis and extract features from data Build models for neural net, linear and non-linear regression, classification, and clustering Evaluate the performance of a model with the right metrics Implement a classification problem using the neural net package Employ a decision tree using the random forest library Who this book is for If you are a data analyst, data scientist, or a business analyst who wants to understand the process of machine learning and apply it to a real dataset using R, this book is just what you need. Data scientists who use Python and want to implement their machine learning solutions using R will also find this book very useful. The book will also enable novice programmers to start their journey in data science. Basic knowledge of any programming language is all you need to get started.
Practical Machine Learning with Rust: Creating Intelligent Applications in Rust
by Joydeep BhattacharjeeExplore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you’ll dive into the more specific fields of machine learning, such as computer vision and natural language processing, and look at the Rust libraries that help create applications for those domains. We will also look at how to deploy these applications either on site or over the cloud. After reading Practical Machine Learning with Rust, you will have a solid understanding of creating high computation libraries using Rust. Armed with the knowledge of this amazing language, you will be able to create applications that are more performant, memory safe, and less resource heavy. What You Will LearnWrite machine learning algorithms in RustUse Rust libraries for different tasks in machine learningCreate concise Rust packages for your machine learning applicationsImplement NLP and computer vision in RustDeploy your code in the cloud and on bare metal servers Who This Book Is For Machine learning engineers and software engineers interested in building machine learning applications in Rust.