Mac eBooks
Machine Learning and Security
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you'll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters; Quickly detect anomalies, including breaches, fraud, and impending system failure; Conduct malware analysis by extracting useful information from computer binaries; Uncover at ...
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you'll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters; Quickly detect anomalies, including breaches, fraud, and impending system failure; Conduct malware analysis by extracting useful information from computer binaries; Uncover at ...
Beginning Xamarin Development for the Mac
Develop apps for the iPhone, iPad, and Apple wearables using Visual Studio for the Mac. Learn how to set up your development environment and emulators, and how to create adaptive user interfaces for various platforms. Expert Dawid Borycki guides you through the fundamentals of programming for Apple platforms (Model View Controller, Test Driven Development), navigation patterns, gesture handling, accessing user's location, and reading and consuming data from web services. After reading this book, you will be able to build native apps that look and feel like other apps built into iOS, watchOS, and tvOS, and have the skills that are in high demand in today's market. If you are already programming C# apps for web or desktop, you will learn how to extend your skill set to Apple mobile, wearable, and smart TV platforms. Build and implement native apps for Apple platforms; Create adaptive, universal views and handle navigation between them; Access user's location and handle touch in ...
Develop apps for the iPhone, iPad, and Apple wearables using Visual Studio for the Mac. Learn how to set up your development environment and emulators, and how to create adaptive user interfaces for various platforms. Expert Dawid Borycki guides you through the fundamentals of programming for Apple platforms (Model View Controller, Test Driven Development), navigation patterns, gesture handling, accessing user's location, and reading and consuming data from web services. After reading this book, you will be able to build native apps that look and feel like other apps built into iOS, watchOS, and tvOS, and have the skills that are in high demand in today's market. If you are already programming C# apps for web or desktop, you will learn how to extend your skill set to Apple mobile, wearable, and smart TV platforms. Build and implement native apps for Apple platforms; Create adaptive, universal views and handle navigation between them; Access user's location and handle touch in ...
Building Intelligent Systems
Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success. This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems. Building Intelligent Systems is based on more than a decade of experience building Internet-scale Intelligent Systems that have hundreds of millions of user interactions per day in some of the largest and most important software systems in the world. Understand the concept of an Intelligent System: What it is good for, when you need one, and how to set it up for success; Design an intelligent user experience: Produce data to help make the Intelligent System better over time; Implement an Intelligent System: Execute, manage, and measure In ...
Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success. This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems. Building Intelligent Systems is based on more than a decade of experience building Internet-scale Intelligent Systems that have hundreds of millions of user interactions per day in some of the largest and most important software systems in the world. Understand the concept of an Intelligent System: What it is good for, when you need one, and how to set it up for success; Design an intelligent user experience: Produce data to help make the Intelligent System better over time; Implement an Intelligent System: Execute, manage, and measure In ...
Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning
Prepare for Microsoft Exam 70-774 - and help demonstrate your real-world mastery of performing key data science activities with Azure Machine Learning services. Designed for experienced IT professionals ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level. Focus on the expertise measured by these objectives: Prepare data for analysis in Azure Machine Learning and export from Azure Machine Learning; Develop machine learning models; Operationalize and manage Azure Machine Learning Services; Use other services for machine learning. ...
Prepare for Microsoft Exam 70-774 - and help demonstrate your real-world mastery of performing key data science activities with Azure Machine Learning services. Designed for experienced IT professionals ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level. Focus on the expertise measured by these objectives: Prepare data for analysis in Azure Machine Learning and export from Azure Machine Learning; Develop machine learning models; Operationalize and manage Azure Machine Learning Services; Use other services for machine learning. ...
Machine Learning with TensorFlow
TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine. Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own. ...
TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine. Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own. ...
Objective-C for Absolute Beginners, 4th Edition
Learn Objective-C and its latest release, and learn how to mix Swift with it. You have a great idea for an app, but how do you bring it to fruition? With Objective-C, the universal language of iPhone, iPad, and Mac apps. Using a hands-on approach, you'll learn how to think in programming terms, how to use Objective-C to construct program logic, and how to synthesize it all into working apps. Gary Bennett, an experienced app developer and trainer, will guide you on your journey to becoming a successful app developer. Along the way you'll discover the flexibility of Apple's developer tools If you're looking to take the first step towards App Store success, Objective-C for Absolute Beginners, Fourth Edition is the place to start. Understand the fundamentals of computer programming: variables, design data structures, and working with file systems; Examine the logic of object-oriented programming: how to use classes, objects, and methods; Install Xcode and write programs in ...
Learn Objective-C and its latest release, and learn how to mix Swift with it. You have a great idea for an app, but how do you bring it to fruition? With Objective-C, the universal language of iPhone, iPad, and Mac apps. Using a hands-on approach, you'll learn how to think in programming terms, how to use Objective-C to construct program logic, and how to synthesize it all into working apps. Gary Bennett, an experienced app developer and trainer, will guide you on your journey to becoming a successful app developer. Along the way you'll discover the flexibility of Apple's developer tools If you're looking to take the first step towards App Store success, Objective-C for Absolute Beginners, Fourth Edition is the place to start. Understand the fundamentals of computer programming: variables, design data structures, and working with file systems; Examine the logic of object-oriented programming: how to use classes, objects, and methods; Install Xcode and write programs in ...
Advanced Data Analytics Using Python
Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You'll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You'll get to know the concepts using Python code, giving you samples to use in your own projects. Work with data analysis techniques such as classification, clustering, regression, and forecasting; Handle structured and unstructured data, ETL techniques, and different kinds of databases such a ...
Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You'll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You'll get to know the concepts using Python code, giving you samples to use in your own projects. Work with data analysis techniques such as classification, clustering, regression, and forecasting; Handle structured and unstructured data, ETL techniques, and different kinds of databases such a ...
Data Science on the Google Cloud Platform
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. Automate and schedule data ingest, using an App Engine application; Create and populate a dashboard in Google Data Studio; Build a real-time analysis pipeline to carry out streaming analytics; Conduct interactive data exploration with Google BigQuery; Create a Bayesian model on a Cloud Dataproc cluster; ...
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. Automate and schedule data ingest, using an App Engine application; Create and populate a dashboard in Google Data Studio; Build a real-time analysis pipeline to carry out streaming analytics; Conduct interactive data exploration with Google BigQuery; Create a Bayesian model on a Cloud Dataproc cluster; ...
Learning Swift, 3rd Edition
Get valuable hands-on experience with Swift, the open source programming language developed by Apple. With this practical guide, skilled programmers with little or no knowledge of Apple development will learn how to code with the latest version of Swift by developing a working iOS app from start to finish. You'll begin with Swift programming basics - including guidelines for making your code "Swifty" - and learn how to work with Xcode and its built-in Interface Builder. Then you'll dive step-by-step into building and customizing a basic app for taking, editing, and deleting selfies. You'll also tune and test the app for performance and manage the app's presence in the App Store. Swift 4 basics: Learn Swift's basic building blocks and the features of object-oriented development; Building the Selfiegram app: Build model objects and the UI for your selfie app and add location support, user settings, and notifications; Polishing Selfiegram: Create a theme and support for sharing and ...
Get valuable hands-on experience with Swift, the open source programming language developed by Apple. With this practical guide, skilled programmers with little or no knowledge of Apple development will learn how to code with the latest version of Swift by developing a working iOS app from start to finish. You'll begin with Swift programming basics - including guidelines for making your code "Swifty" - and learn how to work with Xcode and its built-in Interface Builder. Then you'll dive step-by-step into building and customizing a basic app for taking, editing, and deleting selfies. You'll also tune and test the app for performance and manage the app's presence in the App Store. Swift 4 basics: Learn Swift's basic building blocks and the features of object-oriented development; Building the Selfiegram app: Build model objects and the UI for your selfie app and add location support, user settings, and notifications; Polishing Selfiegram: Create a theme and support for sharing and ...
Introduction to Machine Learning with R
Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you'll first start to learn with regression modelling and then move into more advanced topics such as neural networks and tree-based methods. Finally, you'll delve into the frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression and classification models, you'll be able to solve an array of machine learning problems. Author Scott V. Burger provides several examples to help you build a working knowledge of machine learning. Explore machine learning models, algorithms, and data training; Understand machine learning algorithms for supervised and unsupervised cases; Examine statistical concepts for designing data for use in models; Dive into linear regres ...
Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you'll first start to learn with regression modelling and then move into more advanced topics such as neural networks and tree-based methods. Finally, you'll delve into the frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression and classification models, you'll be able to solve an array of machine learning problems. Author Scott V. Burger provides several examples to help you build a working knowledge of machine learning. Explore machine learning models, algorithms, and data training; Understand machine learning algorithms for supervised and unsupervised cases; Examine statistical concepts for designing data for use in models; Dive into linear regres ...
Machine Learning with Python Cookbook
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. Vectors, matrices, and arrays; Handling numerical and categorical data, text, images, and dates and times; Dimensionality reduction using feature extracti ...
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. Vectors, matrices, and arrays; Handling numerical and categorical data, text, images, and dates and times; Dimensionality reduction using feature extracti ...