{"product_id":"machine-learning-for-ios-developers-isbn-9781119602873","title":"Machine Learning for iOS Developers","description":"\u003cp\u003e\u003cb\u003eHarness 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!\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMachine 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, \u003ci\u003eMachine Learning for iOS Developers \u003c\/i\u003eis an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications.\u003c\/p\u003e \u003cp\u003eAssuming 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:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eUnderstand the theoretical concepts and practical applications of machine learning used in predictive data analytics\u003c\/li\u003e \u003cli\u003eBuild, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming\u003c\/li\u003e \u003cli\u003eDevelop skills in data acquisition and modeling, classification, and regression.\u003c\/li\u003e \u003cli\u003eCompare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS)\u003c\/li\u003e \u003cli\u003eImplement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn \u0026amp; Keras models with CoreML\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMachine Learning for iOS Developers\u003c\/i\u003e is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps.\u003c\/p\u003e \u003cp\u003eIntroduction xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1 Fundamentals of Machine Learning 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Introduction to Machine Learning 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat is Machine Learning? 4\u003c\/p\u003e \u003cp\u003eTools Commonly Used by Data Scientists 4\u003c\/p\u003e \u003cp\u003eCommon Terminology 5\u003c\/p\u003e \u003cp\u003eReal-World Applications of Machine Learning 7\u003c\/p\u003e \u003cp\u003eTypes of Machine Learning Systems 8\u003c\/p\u003e \u003cp\u003eSupervised Learning 9\u003c\/p\u003e \u003cp\u003eUnsupervised Learning 10\u003c\/p\u003e \u003cp\u003eSemisupervised Learning 11\u003c\/p\u003e \u003cp\u003eReinforcement Learning 11\u003c\/p\u003e \u003cp\u003eBatch Learning 12\u003c\/p\u003e \u003cp\u003eIncremental Learning 12\u003c\/p\u003e \u003cp\u003eInstance-Based Learning 13\u003c\/p\u003e \u003cp\u003eModel-Based Learning 13\u003c\/p\u003e \u003cp\u003eCommon Machine Learning Algorithms 13\u003c\/p\u003e \u003cp\u003eLinear Regression 14\u003c\/p\u003e \u003cp\u003eSupport Vector Machines 15\u003c\/p\u003e \u003cp\u003eLogistic Regression 19\u003c\/p\u003e \u003cp\u003eDecision Trees 21\u003c\/p\u003e \u003cp\u003eArtificial Neural Networks 23\u003c\/p\u003e \u003cp\u003eSources of Machine Learning Datasets 24\u003c\/p\u003e \u003cp\u003eScikit-learn Datasets 24\u003c\/p\u003e \u003cp\u003eAWS Public Datasets 27\u003c\/p\u003e \u003cp\u003eKaggle.com Datasets 27\u003c\/p\u003e \u003cp\u003eUCI Machine Learning Repository 27\u003c\/p\u003e \u003cp\u003eSummary 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 The Machine-Learning Approach 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Traditional Rule-Based Approach 29\u003c\/p\u003e \u003cp\u003eA Machine-Learning System 33\u003c\/p\u003e \u003cp\u003ePicking Input Features 34\u003c\/p\u003e \u003cp\u003ePreparing the Training and Test Set 39\u003c\/p\u003e \u003cp\u003ePicking a Machine-Learning Algorithm 40\u003c\/p\u003e \u003cp\u003eEvaluating Model Performance 41\u003c\/p\u003e \u003cp\u003eThe Machine-Learning Process 44\u003c\/p\u003e \u003cp\u003eData Collection and Preprocessing 44\u003c\/p\u003e \u003cp\u003ePreparation of Training, Test, and Validation Datasets 44\u003c\/p\u003e \u003cp\u003eModel Building 45\u003c\/p\u003e \u003cp\u003eModel Evaluation 45\u003c\/p\u003e \u003cp\u003eModel Tuning 45\u003c\/p\u003e \u003cp\u003eModel Deployment 46\u003c\/p\u003e \u003cp\u003eSummary 46\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Data Exploration and Preprocessing 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Preprocessing Techniques 47\u003c\/p\u003e \u003cp\u003eObtaining an Overview of the Data 47\u003c\/p\u003e \u003cp\u003eHandling Missing Values 57\u003c\/p\u003e \u003cp\u003eCreating New Features 60\u003c\/p\u003e \u003cp\u003eTransforming Numeric Features 62\u003c\/p\u003e \u003cp\u003eOne-Hot Encoding Categorical Features 64\u003c\/p\u003e \u003cp\u003eSelecting Training Features 65\u003c\/p\u003e \u003cp\u003eCorrelation 65\u003c\/p\u003e \u003cp\u003ePrincipal Component Analysis 68\u003c\/p\u003e \u003cp\u003eRecursive Feature Elimination 70\u003c\/p\u003e \u003cp\u003eSummary 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Implementing Machine Learning on Mobile Apps 73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDevice-Based vs Server-Based Approaches 73\u003c\/p\u003e \u003cp\u003eApple’s Machine Learning Frameworks and Tools 75\u003c\/p\u003e \u003cp\u003eTask-Level Frameworks 75\u003c\/p\u003e \u003cp\u003eModel-Level Frameworks 76\u003c\/p\u003e \u003cp\u003eFormat Converters 76\u003c\/p\u003e \u003cp\u003eTransfer Learning Tools 77\u003c\/p\u003e \u003cp\u003eThird-Party Machine-Learning Frameworks and Tools 78\u003c\/p\u003e \u003cp\u003eSummary 79\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2 Machine Learning with CoreML, CreateML, and TuriCreate 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Object Detection Using Pre- trained Models 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat is Object Detection? 83\u003c\/p\u003e \u003cp\u003eA Brief Introduction to Artificial Neural Networks 86\u003c\/p\u003e \u003cp\u003eDownloading the ResNet50 Model 92\u003c\/p\u003e \u003cp\u003eCreating the iOS Project 92\u003c\/p\u003e \u003cp\u003eCreating the User Interface 95\u003c\/p\u003e \u003cp\u003eUpdating Privacy Settings 100\u003c\/p\u003e \u003cp\u003eUsing the Resnet50 Model in the iOS Project 100\u003c\/p\u003e \u003cp\u003eSummary 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Creating an Image Classifier with the Create ML App 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction to the Create ML App 112\u003c\/p\u003e \u003cp\u003eCreating the Image Classification Model with the Create ML App 113\u003c\/p\u003e \u003cp\u003eCreating the iOS Project 117\u003c\/p\u003e \u003cp\u003eCreating the User Interface 118\u003c\/p\u003e \u003cp\u003eUpdating Privacy Settings 122\u003c\/p\u003e \u003cp\u003eUsing the Core ML Model in the iOS Project 123\u003c\/p\u003e \u003cp\u003eSummary 132\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Creating a Tabular Classifier with Create ML 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePreparing the Dataset for the Create ML App 135\u003c\/p\u003e \u003cp\u003eCreating the Tabular Classification Model with the Create ML App 143\u003c\/p\u003e \u003cp\u003eCreating the iOS Project 147\u003c\/p\u003e \u003cp\u003eCreating the User Interface 148\u003c\/p\u003e \u003cp\u003eUsing the Classification Model in the iOS Project 156\u003c\/p\u003e \u003cp\u003eTesting the App 172\u003c\/p\u003e \u003cp\u003eSummary 173\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Creating a Decision Tree Classifier r 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDecision Tree Recap 175\u003c\/p\u003e \u003cp\u003eExamining the Dataset 176\u003c\/p\u003e \u003cp\u003eCreating Training and Test Datasets 180\u003c\/p\u003e \u003cp\u003eCreating the Decision Tree Classification Model with Scikit-learn 181\u003c\/p\u003e \u003cp\u003eUsing Core ML Tools to Convert the Scikit-learn Model to the Core ML Format 186\u003c\/p\u003e \u003cp\u003eCreating the iOS Project 187\u003c\/p\u003e \u003cp\u003eCreating the User Interface 188\u003c\/p\u003e \u003cp\u003eUsing the Scikit-learn Decision Tree Classifier Model in the iOS Project 193\u003c\/p\u003e \u003cp\u003eTesting the App 201\u003c\/p\u003e \u003cp\u003eSummary 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Creating a Logistic Regression Model Using Scikit-learn and Core ML 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExamining the Dataset 203\u003c\/p\u003e \u003cp\u003eCreating a Training and Test Dataset 208\u003c\/p\u003e \u003cp\u003eCreating the Logistic Regression Model with Scikit-learn 210\u003c\/p\u003e \u003cp\u003eUsing Core ML Tools to Convert the Scikit-learn Model to the Core ML Format 216\u003c\/p\u003e \u003cp\u003eCreating the iOS Project 218\u003c\/p\u003e \u003cp\u003eCreating the User Interface 219\u003c\/p\u003e \u003cp\u003eUsing the Scikit-learn Model in the iOS Project 225\u003c\/p\u003e \u003cp\u003eTesting the App 232\u003c\/p\u003e \u003cp\u003eSummary 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Building a Deep Convolutional Neural Network with Keras 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction to the Inception Family of Deep Convolutional Neural Networks 236\u003c\/p\u003e \u003cp\u003eGoogLeNet (aka Inception-v1) 236\u003c\/p\u003e \u003cp\u003eInception-v2 and Inception-v3 238\u003c\/p\u003e \u003cp\u003eInception-v4 and Inception-ResNet 239\u003c\/p\u003e \u003cp\u003eA Brief Introduction to Keras 244\u003c\/p\u003e \u003cp\u003eImplementing Inception-v4 with the Keras Functional API 246\u003c\/p\u003e \u003cp\u003eTraining the Inception-v4 Model 259\u003c\/p\u003e \u003cp\u003eExporting the Keras Inception-v4 Model to the Core ML Format 269\u003c\/p\u003e \u003cp\u003eCreating the iOS Project 270\u003c\/p\u003e \u003cp\u003eCreating the User Interface 271\u003c\/p\u003e \u003cp\u003eUpdating Privacy Settings 276\u003c\/p\u003e \u003cp\u003eUsing the Inception-v4 Model in the iOS Project 277\u003c\/p\u003e \u003cp\u003eSummary 286\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Anaconda and Jupyter Notebook Setup 287\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eInstalling the Anaconda Distribution 287\u003c\/p\u003e \u003cp\u003eCreating a Conda Python Environment 288\u003c\/p\u003e \u003cp\u003eInstalling Python Packages 291\u003c\/p\u003e \u003cp\u003eInstalling Jupyter Notebook 293\u003c\/p\u003e \u003cp\u003eSummary 296\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Introduction to NumPy and Pandas 297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNumPy 297\u003c\/p\u003e \u003cp\u003eCreating NumPy Arrays 297\u003c\/p\u003e \u003cp\u003eModifying Arrays 301\u003c\/p\u003e \u003cp\u003eIndexing and Slicing 304\u003c\/p\u003e \u003cp\u003ePandas 305\u003c\/p\u003e \u003cp\u003eCreating Series and Dataframes 305\u003c\/p\u003e \u003cp\u003eGetting Dataframe Information 307\u003c\/p\u003e \u003cp\u003eSelecting Data 311\u003c\/p\u003e \u003cp\u003eSummary 313\u003c\/p\u003e \u003cp\u003eIndex 315\u003c\/p\u003e   \u003cp\u003e\u003cb\u003eAbhishek Mishra\u003c\/b\u003e has more than 19 years of experience across a broad range of mobile and enterprise technologies. He consults as a security and fraud solution architect with Lloyds Banking group PLC in London. He is the author of Machine Learning on the AWS Cloud, \u003ci\u003eAmazon Web Services for Mobile Developers\u003c\/i\u003e, iOS Code Testing, and Swift iOS: 24-Hour Trainer.    \u003c\/p\u003e\u003cp\u003e\u003cb\u003eLearn how to harness machine learning in your iOS apps!\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eMachine Learning (ML) is a discipline within Artificial Intelligence (AI) that enables computers to learn patterns from data and use these patterns to categorize and predict information reliably and quickly without being explicitly programmed. The Apple iOS Software Development Kit (SDK) includes the Core ML framework that allows developers to integrate ML services into both mobile and desktop applications. Assuming no prior experience in the field, \u003ci\u003eMachine Learning for iOS Developers\u003c\/i\u003e covers building machine learning models with Apple's new Create ML app, as well as building models using popular Python libraries such as NumPy, Pandas, Scikit-learn and Keras. The book then teaches you how to integrate these models in iOS applications using Apple's Core ML framework and the Core ML tools library. Step-by-step guidance, hands-on activities, real-world scenarios, and downloadable source code examples make \u003ci\u003eMachine Learning for iOS Developers\u003c\/i\u003e a must-have resource for anyone wishing to learn the concepts and techniques needed to be a successful Apple iOS Machine Learning practitioner. \u003c\/p\u003e\u003cp\u003eThis practical, clearly-written guide shows how to: \u003c\/p\u003e\u003cul\u003e \u003cli\u003e\u003cb\u003eUnderstand ML data collection, preprocessing, and feature engineering.\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eLearn fundamental machine learning algorithms.\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eCreate machine learning models with the new CreateML App.\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eCreate machine learning models with Scikit-learn and CoreML tools.\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eIntegrate Core ML models in iOS Applications.\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eUse pre-trained machine learning models.\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eCreate a deep learning network from scratch with Keras and use it in an iOS app.\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eCreate decision trees and random forest models.\u003c\/b\u003e\u003c\/li\u003e \u003c\/ul\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989548613861,"sku":"NP9781119602873","price":50.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119602873.jpg?v=1761784553","url":"https:\/\/k12savings.com\/products\/machine-learning-for-ios-developers-isbn-9781119602873","provider":"K12savings","version":"1.0","type":"link"}