دانلود کتاب تسلط بر یادگیری ماشین Azure – ویرایش دوم

During the last decade, machine learning (ML) has grown from a niche concept worked on in scientific circles to an enterprise-grade toolset that can be used to improve business processes and build intelligent products and services. The main reason is the constant increase in the volume of data being generated globally, requiring distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights. This book will help you improve your knowledge of ML concepts, find the right models for your use cases, and will give you the skillset to run machine learning models and build end-to-end ML pipelines in the Azure cloud. The book starts with an overview of every step in an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. From there on out, it focuses on the Azure Machine Learning service and takes you through the important processes of data preparation and feature engineering. Following that, the book focuses on ML modeling techniques for different requirements, including advanced feature extraction techniques using natural language processing (NLP), classical ML techniques such as ensemble learning, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. In addition, the book explores how to train, optimize, and tune models using Azure automated machine learning and HyperDrive, and perform model training on distributed training clusters on Azure. Finally, the book covers the deployment of ML models to different target computes such as Azure Machine Learning clusters, Azure Kubernetes Service, and Field Programmable Gate Arrays (FPGAs), along with the setup of MLOps pipelines with Azure DevOps. By the end of this book, you’ll have the foundation to run a well-thought-out ML project from start to finish and will have mastered the tooling available in Azure to train, deploy, and operate ML models and pipelines.