دانلود کتاب راهنمای تخصصی یادگیری ماشین دارای گواهینامه AWS

The machine learning revolution is transforming industries across the globe, much like the advent of electricity once did. As organizations increasingly rely on data-driven insights and intelligent automation, the demand for skilled machine learning professionals who can harness the power of cloud computing has never been greater. Amazon Web Services has emerged as the leading platform for building, deploying, and managing machine learning solutions at scale. This book is designed to be your comprehensive guide to mastering machine learning on AWS and successfully passing the AWS Certified Machine Learning – Specialty exam. It bridges the gap between fundamental cloud computing knowledge and advanced machine learning expertise, taking you on a journey from understanding basic concepts to building production-ready ML solutions. Throughout this guide, you will gain hands-on experience with essential AWS services, including Amazon SageMaker, AWS Glue, Amazon Kinesis, AWS Lambda, and many others. The book is structured around the four key domains of the AWS ML Specialty certification, that is,data engineering, exploratory data analysis, modeling, and machine learning implementation and operations. Each chapter builds upon previous concepts while providing practical, real-world examples that you can apply immediately. This book is intended for aspiring machine learning specialists, data scientists, data engineers, cloud architects, and professionals seeking to validate their expertise in AWS machine learning technologies. Whether you are beginning your machine learning journey or looking to formalize your existing knowledge, this guide will equip you with the skills and confidence needed to excel in the rapidly evolving field of cloud-based machine learning. With this book, you will not only prepare for certification success but also develop the practical skills necessary to drive innovation and make a meaningful impact in your organization through the power of AWS machine learning. Chapter 1: Creating Data Repositories for Machine Learning- This chapter establishes the foundation for any ML project by exploring how to identify diverse data sources and select appropriate storage solutions on AWS. The chapter covers databases, Amazon S3, Amazon EFS, and Amazon EBS, providing best practices for data repository design and integration strategies that ensure your data infrastructure can support robust machine learning workflows from simple batch processing to complex realtime analytics. Chapter 2: Implementing Data Ingestion Solutions- This chapter discusses the critical process of moving data into your ML pipeline, covering both batch and streaming data ingestion patterns. You wi’ll learn to leverage Amazon Kinesis, Amazon EMR, AWS Glue, and other AWS services to orchestrate and automate data pipelines, including scheduling and managing complex data ingestion jobs for various data types and volumes across different organizational needs. Chapter 3: Transforming Data into Insights– This chapter focuses on converting raw data into formats suitable for machine learning analysis. The chapter explores ETL processes using AWS Glue and Amazon EMR, handling ML-specific data transformations with MapReduce, Apache Hadoop, Spark, and Hive, while providing optimization techniques to prepare data for various ML algorithms and ensuring scalable transformation workflows. Chapter 4: Data Sanitization and Preparation- This chapter addresses the crucial task of ensuring data quality and readiness for modeling. You will learn to identify and handle missing or corrupt data, implement data cleaning and preprocessing techniques, and apply normalization and scaling methods. The chapter emphasizes data augmentation strategies and quality assessment practices essential for successful ML outcomes while maintaining data integrity throughout the preparation process. Chapter 5: Feature Engineering- This chapter explores the art and science of extracting meaningful features from diverse data sources including text, speech, and images. The chapter covers feature identification techniques, dimensionality reduction methods, and feature transformation approaches, with practical examples demonstrating how to enhance your datasets for optimal ML model performance using AWS tools like SageMaker Feature Store and processing capabilities. Chapter 6: Data Analysis and Visualization- This chapter teaches you to create insightful visualizations and interpret key statistics that inform ML decision-making. You will learn to generate various graph types, understand descriptive statistics, implement cluster analysis for data segmentation, and utilize AWS visualization tools including QuickSight to effectively communicate data insights to stakeholders and validate your analytical assumptions. Chapter 7: Framing Business Problems as ML Problems- This chapter bridges the gap between business challenges and technical ML solutions. The chapter helps you assess when ML is appropriate, differentiate between supervised and unsupervised learning approaches, and select suitable models for various business scenarios through real-world case studies and best practices for problem definition, ensuring alignment between business objectives and technical implementation. Chapter 8: Selecting Appropriate ML Models- This chapter provides comprehensive coverage of the ML model landscape, including XGBoost, logistic regression, decision trees, and neural networks such as RNNs and CNNs. You will develop intuition about model selection criteria based on data characteristics and problem types, while learning to leverage AWS tools and SageMaker’s built-in algorithms for effective model implementation and comparison. Chapter 9: Training ML Models- This chapter covers methodologies and best practices for effective model training, including data splitting strategies, optimization techniques, and compute resource selection. The chapter addresses GPU vs CPU considerations, Spark and non-Spark platforms, and provides guidance on updating and retraining strategies to maintain model relevance using SageMaker training jobs and distributed training capabilities. Chapter 10: Hyperparameter Optimization- This chapter focuses on refining ML models for peak performance through systematic tuning approaches. You will learn regularization techniques including dropout and L1/L2 regularization, cross-validation methods, neural network architecture optimization, and tree-based model tuning. The chapter demonstrates how to leverage AWS solutions like SageMaker Automatic Model Tuning for efficient hyperparameter optimization at scale. Chapter 11: Evaluating ML Models- This chapter centers on comprehensive model evaluation techniques to ensure optimal performance and avoid common pitfalls. The chapter covers detecting and handling bias and variance, understanding evaluation metrics such as AUC-ROC, precision, recall, and F1 score, and implementing both offline and online evaluation strategies using AWS tools for continuous model assessment and validation. Chapter 12: Building ML Solutions for Performance and Scalability- This chapterdiscusses the creation of machine learning solutions that are high-performing, scalable, resilient, and fault-tolerant. You will explore monitoring with AWS CloudTrail and Amazon CloudWatch, deploying solutions across multiple regions and availability zones, creating and managing AMIs and Docker containers, implementing auto-scaling, and following AWS best practices for enterprise-grade ML deployments. Chapter 13: Recommending and Implementing Appropriate ML Services- This chapter teaches you to choose and implement the most suitable AWS machine learning services for specific scenarios. The chapter covers AWS ML application services including Amazon Polly, Lex, and Transcribe, understanding service quotas, making build-versus-buy decisions with SageMaker built-in algorithms, and optimizing costs through strategic use of spot instances and AWS Batch for deep learning workloads. Chapter 14: Applying AWS Security Practices to ML Solutions- This chapter focuses on implementing fundamental AWS security practices essential for production ML systems. You will learn about IAM roles and policies for ML workflows, S3 bucket security configurations, VPC networking for secure deployments, and encryption and anonymization techniques to protect sensitive data throughout the ML pipeline while maintaining compliance with organizational security requirements. Chapter 15: Deploying and Operationalizing ML Solutions- This chapter covers the complete lifecycle of ML model deployment and operational management. The chapter addresses exposing and interacting with ML endpoints, implementing A/B testing strategies, establishing retraining pipelines, and debugging and troubleshooting techniques to ensure models continue performing optimally in production environments using SageMaker endpoints and monitoring capabilities. Appendix- This chapter provides a comprehensive practice test that simulates the actual AWS Certified Machine Learning – Specialty exam experience. This chapter includes sample questions across all four domains, detailed explanations for correct and incorrect answers, and strategic guidance for exam preparation, helping you assess your readiness and identify areas requiring additional study before taking the certification exam.