Ultimate Azure Data Scientist Associate (DP-100) Certification GuideКНИГИ » ПРОГРАММИНГ
Название: Ultimate Azure Data Scientist Associate (DP-100) Certification Guide: Simplified Concepts and Effective ML Solutions to Crack the Azure Data Scientist DP-100 Exam Автор: Rajib Kumar De Издательство: Orange Education Pvt Ltd, AVA Год: 2024 Страниц: 807 Язык: английский Формат: pdf, epub Размер: 25.6 MB
Empower Your Data Science Journey: From Exploration to Certification in Azure Machine LearningBook DescriptionUltimate Azure Data Scientist Associate (DP-100) Certification Guide is your essential resource for achieving the Microsoft Azure Data Scientist Associate certification. This guide covers all exam objectives, helping you design and prepare machine learning solutions, explore data, train models, and manage deployment and retraining processes.The book starts with the basics and advances through hands-on exercises and real-world projects, to help you gain practical experience with Azure's tools and services. The book features certification-oriented Q&A challenges that mirror the actual exam, with detailed explanations to help you thoroughly grasp each topic. Perfect for aspiring data scientists, IT professionals, and analysts, this comprehensive guide equips you with the expertise to excel in the DP-100 exam and advance your Data Science career.
In the fast-evolving landscape of Azure data science, embracing a framework that seamlessly blends power with elegance is paramount. Welcome to the modern cloud-based data science world — a journey that transcends the ordinary and propels you into the extraordinary.
This book comprises 12 chapters, each a complete module in itself, serving as your comprehensive guide to mastering data science on Azure. It covers a wide array of topics, from the fundamentals to advanced techniques. Whether you are a seasoned developer looking to expand your skills or a newcomer eager to dive into data science, this book has something for everyone.
Chapter 1. Introduction to Data Science and Azure: In this chapter, you will be introduced to the world of data science and Microsoft Azure. You will understand the significance of data science in today's business landscape and explore the capabilities that Azure offers for data science projects. Topics include an overview of data science concepts, Azure's role in data science, and setting the stage for your journey in designing and implementing data science solutions on the Azure platform.
Chapter 2. Setting Up Your Azure Environment: This chapter focuses on the practical aspects of getting started with Azure. You will learn how to create an Azure account and subscription and gain insights into relevant Azure services for data science. The core part of this chapter is setting up your Azure Machine Learning workspace, which serves as a hub for managing and running data science experiments.
Chapter 3. Data Ingestion and Storage in Azure: Understanding data ingestion is pivotal. You will delve into Azure Data Lake Storage and Azure SQL Database, exploring how these services facilitate data storage and management. The chapter covers various techniques for data ingestion, including batch and real-time methods, ensuring a holistic understanding of data movement in Azure.
Chapter 4. Data Transformation and Cleaning: Effective data transformation and cleaning are essential for accurate analysis. You will grasp the significance of preprocessing and learn how to use Azure Data Factory for data transformation. The chapter also delves into data cleaning techniques using both Python and Azure services, ensuring your data is ready for analysis.
Chapter 5. Introduction to Machine Learning: This chapter provides a foundational understanding of machine learning. It covers the basics of supervised and unsupervised learning algorithms, giving you a clear grasp of the fundamental concepts that underpin machine learning techniques.
Chapter 6. Azure Machine Learning Studio: Explore Azure Machine Learning Studio in-depth. This includes creating and training machine learning models within the studio, understanding hyperparameter tuning, and evaluating model performance. The chapter empowers you to harness the power of Azure's machine learning capabilities effectively.
Chapter 7. Model Deployment and Monitoring: Deploying and monitoring machine learning models is critical for successful integration. This chapter guides you through deploying models using Azure services, ensuring you understand the steps involved. You will also learn about monitoring and managing deployed models, as well as integrating CI/CD practices for machine learning projects.
Chapter 8. Embracing AI Revolution Azure: This enlightening chapter delves comprehensively into the Generative AI and Large Language Models, focusing on their implementation and application using Microsoft Azure's robust tools and services. Beginning with an exploration of Azure's advanced capabilities like OpenAI, Semantic Kernel, and the Model Catalog, we further examine the utilization of Generative AI in various applications. The chapter also dives deep into the nuances of creating engaging conversational experiences with Azure's PromptFlow and wraps up by addressing critical aspects such as Responsible AI. Moreover, it encompasses a detailed walkthrough on how to leverage Azure's multi-modal AI services, including Azure Speech and Azure Vision, to build more inclusive AI models.
Chapter 9. Responsible AI and Ethics: The ethical considerations of AI and data science are explored in this chapter. Learn about responsible AI practices, including fairness, transparency, and bias mitigation, ensuring that your data science solutions adhere to ethical standards.
Chapter 10. Big Data Analytics with Azure: This chapter introduces you to big data analytics using Azure. Gain an understanding of Apache Spark and how Azure Databricks facilitates big data processing and analytics. Topics covered include big data concepts, Databricks architecture, and real-world applications.
Chapter 11. Real-World Applications and Case Studies: Dive into real-world applications of data science on Azure. This chapter showcases case studies that highlight successful implementations of data science solutions, providing valuable insights into how these concepts are put into practice.
Chapter 12. Conclusion and Next Steps: The concluding chapter recaps the key takeaways from the book and encourages you to continue your journey in data science on Azure. It offers guidance on further enhancing your skills and exploring advanced topics.
This book is a hands-on guide filled with practical examples, real-world scenarios, and best practices. We hope this journey through Azure empowers you to build cutting-edge machine learning applications and enhances your skills in the dynamic field of Data Science.
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