Design of Intelligent Applications Using Machine Learning and Deep Learning Techniques - Info and Reading Options

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Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1. Data Acquisition and Preparation for Artificial Intelligence and Machine Learning Applications -- 1.1 Introduction -- 1.2 Reference Architecture -- 1.2.1 Data Sources -- 1.2.2 Data Storage -- 1.2.3 Batch Processing -- 1.2.4 Real-Time Message Ingestion -- 1.2.5 Stream Processing -- 1.2.6 Machine Learning -- 1.2.7 Analytical Data Store -- 1.2.8 Analytics and Reports -- 1.2.9 Orchestration -- 1.3 Data Acquisition Layer -- 1.3.1 File Systems -- 1.3.2 Databases -- 1.3.3 Applications -- 1.3.4 Devices -- 1.3.5 Enterprise Data Gateway -- 1.3.6 Field Gateway -- 1.3.7 Data Integration Services -- 1.3.8 Data Ingestion Services -- 1.4 Data Ingestion Layer -- 1.4.1 Data Storage Layer -- 1.4.2 Landing Layer -- 1.4.3 Cleansed Layer -- 1.4.4 Processed Layer -- 1.4.5 Data Processing Layer -- 1.4.6 Data Processing Engine -- 1.4.7 Data Processing Programs -- 1.4.8 Scheduling Engine -- 1.4.9 Scheduling Scripts -- 1.5 Data Quality and Cleansing Layer -- 1.5.1 Master Data Management (MDM)System -- 1.5.2 Master Data Management (MDM)Referencing Programs -- 1.5.3 Data Quality Check Programs -- 1.5.4 Rejected/Quarantined Layer -- Bibliography -- 2. Fundamental Models in Machine Learning and Deep Learning -- 2.1 Introduction -- 2.2 Classification of Machine Learning Models -- 2.2.1 Supervised Learning -- 2.2.2 Unsupervised Learning -- 2.2.3 Semi-Supervised Learning -- 2.2.4 Reinforcement Learning -- 2.3 Fundamental Supervised Learning Models -- 2.3.1 Regression -- 2.3.2 Classification -- 2.3.2.1 Logistic Regression -- 2.3.2.2 Support Vector Machines -- 2.3.3 Classification-Regression -- 2.3.3.1 Decision Tree -- 2.3.3.2 Random Forest -- 2.3.3.3 Artificial Neural Network

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