Implement machine learning models in your iOS applications. This short work begins by reviewing the primary principals of machine learning and then moves on to discussing more advanced topics, such as CoreML, the framework used to enable machine learning tasks in Apple products. Many applications on iPhone use machine learning: Siri to serve voice-based requests, the Photos app for facial recognition, and Facebook to suggest which people that might be in a photo. You'll review how these types of machine learning tasks are implemented and performed so that you can use them in your own apps. Beginning Machine Learning in iOS is your guide to putting machine learning to work in your iOS applications. What You'll LearnUnderstand the CoreML components Train custom models Implement GPU processing for better computation efficiency Enable machine learning in your application Who This Book Is For Novice developers and programmers who wish to implement machine learning in their iOS applications and those who want to learn the fundamentals about machine learning.

This book presents the fundamentals of mobile visual computing in iOS development and provides directions for developers and researchers interested in developing iOS applications with image processing and computer vision capabilities. Presenting a technical overview of some of the tools, languages, libraries, frameworks, and APIs currently available for developing iOS applications Image Processing and Computer Vision in iOS reveals the rich capabilities in image processing and computer vision. Its main goal is to provide a road map to what is currently available, and a path to successfully tackle this rather complex but highly rewarding task.

Machine learning is a field of Artificial intelligence that provides algorithms those can learn and improve from experiences. Machine learning algorithms are turned as integral parts of today’s digital life. Its applications include recommender systems, targeted campaigns, text categorization, computer vision and auto security systems etc. Machine learning also considered as essential part of data science due to its capability of providing predictive analytics, capability in handling variety of data and suitability for big data applications. Its capability for predictive analytics resulted of its general structure that is building statistical models out of training data. In other hand easy scalability advantage of machine learning algorithms is making them to be suitable for big data applications. The different types of learning algorithms includes supervised learning, unsupervised learning, reinforcement learning, feature learning, rule based learning, Robot or expert system learning, sparse dictionary and anomaly detection. These learning algorithms can be realized by computing models artificial neural networks, decision trees, support vector machines, regression analysis, Bayesian networks, Genetic algorithms and soft computing. The familiar tools to implement machine learning algorithms include Python, R, Matlab, Scala, Clojure and Ruby. Involving of such open source programming languages, tools and social network communities makes the machine learning most progressing filed of computer science. The machine learning life cycle includes defining project objectives, explore the types and format, modeling data to fit for machine learning algorithms, deciding suitable machine learning model and implement and decide best result from data for decision making. These days, machine learning is observing great interest by the society and it has turned as one of the significant responsibility of top level managers to transform their business in the profitable means by exploring its basic functionalities. The world is at the sheer of realizing a situation where machines will work in agreement with human being to work together, operation, and advertise their services in a novel way which is targeted, valuable, and well-versed. In order to achieve this, they can influence machine learning distinctiveness. Dr. Raghuram Bhukya

Learn iOS app development from scratch and build your dream app KEY FEATURES ● Experience the cutting-edge capabilities of Xcode 15 and Swift 5.9 with this enhanced edition, unraveling the latest features. ● Embark on an exciting journey into the world of iOS programming while enjoying the process of building your very own iOS apps. ● Uncover the exciting advancements in iOS 17, including SwiftData, ActivityKit, SwiftUI, CoreML, and the Symbol Framework. DESCRIPTION “iOS 17 App Development for Beginners” is a definitive guide to building iOS apps with Swift. This book teaches the fundamentals of Swift, laying the foundation for future app development. It covers how to develop user interfaces for iOS apps using SwiftUI and UIKit and how to write code for views, view controllers, and data managers. The book also teaches using Core Data, Swift Data, and SQLite for database storage. Additionally, it covers essential Apple technologies and frameworks, such as Core Location and MapKit for GPS tracking, Camera and Photo Library for image storage, CI/CD, and Core ML for machine learning and artificial intelligence solutions. After completing this book, you will have a solid grasp of Swift app development and successfully publish your apps to the App Store. WHAT YOU WILL LEARN ● Explore the enhancements in the Swift programming language. ● Discover how to seamlessly integrate and manage complex data models using SwiftData and Core Data. ● Take a deep dive into the declarative and intuitive SwiftUI framework. ● Learn how to integrate machine learning with Core ML into your apps. ● Integrate ActivityKit to create engaging and interactive experiences within your iOS 17 apps. WHO THIS BOOK IS FOR This book is an excellent resource for anyone who wants to learn how to program in Swift and develop applications for the iOS platform. Whether you are a beginner, a student, or a professional, this book will teach you the basics of Swift and how to use it to create your apps. No prior programming experience is necessary, but some familiarity with other programming languages will be helpful. TABLE OF CONTENTS 1. Getting Started with Xcode 2. Swift Fundamentals 3. Class, Structure, and Enumerations 4. Protocols, Extensions, and Error Handling 5. Automatic Reference Counting and Memory Safety 6. Implementing iOS 17 Architecture 7. User Interface Design with UIKit 8. User Interface Design with SwiftUI 9. Concurrency in Swift and SwiftUI 10. Storing Data with SQLite and Core Data 11. File Handling in iOS 12. Core Location with MapKit 13. Camera and Photo Library 14. Multithreading in iOS 15. Networking in iOS Apps 16. Mobile App Architectures, Patterns, and Anti-Patterns 17. Publish iOS App on the Apple App Store 18. Continuous Integration and Delivery with Xcode Cloud 19. Advance iOS with New Frameworks

Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple’s ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications. Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book’s clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models—both pre-trained and user-built—with Apple’s CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers: Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming Develop skills in data acquisition and modeling, classification, and regression. Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS) Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn & Keras models with CoreML Machine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps.

This book presents an overview of how machine learning and data mining techniques are used for tracking and preventing diseases. It covers several aspects such as stress level identification of a person from his/her speech, automatic diagnosis of disease from X-ray images, intelligent diagnosis of Glaucoma from clinical eye examination data, prediction of protein-coding genes from big genome data, disease detection through microscopic analysis of blood cells, information retrieval from electronic medical record using named entity recognition approaches, and prediction of drug-target interactions. The book is suitable for computer scientists having a bachelor degree in computer science. The book is an ideal resource as a reference book for teaching a graduate course on AI for Medicine or AI for Health care. Researchers working in the multidisciplinary areas use this book to discover the current developments. Besides its use in academia, this book provides enough details about the state-of-the-art algorithms addressing various biomedical domains, so that it could be used by industry practitioners who want to implement AI techniques to analyze the diseases. Medical institutions use this book as reference material and give tutorials to medical experts on how the advanced AI and ML techniques contribute to the diagnosis and prediction of the diseases.

Deep learning has become a trending area of research due to its adaptive characteristics and high levels of applicability. In recent years, researchers have begun applying deep learning strategies to image analysis and pattern recognition for solving technical issues within image classification. As these technologies continue to advance, professionals have begun translating this intelligent programming language into mobile applications for devices. Programmers and web developers are in need of significant research on how to successfully develop pattern recognition applications using intelligent programming. MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and Opportunities is an essential reference source that presents a solution to developing intelligent pattern recognition Apps on iOS devices based on MatConvNet deep learning. Featuring research on topics such as medical image diagnosis, convolutional neural networks, and character classification, this book is ideally designed for programmers, developers, researchers, practitioners, engineers, academicians, students, scientists, and educators seeking coverage on the specific development of iOS mobile applications using pattern recognition strategies.