Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you'll learn how to apply Automated Machine Learning, a process that uses machine learning to help people build machine learning models. Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of technical depth, hands-on examples, and case studies that show how customers are solving real-world problems with this technology.
Building machine learning models is an iterative and time-consuming process. Even those who know how to create these models may be limited in how much they can explore. Once you complete this book, you'll understand how to apply Automated Machine Learning to your data right away.
Learn how companies in different industries are benefiting from Automated Machine Learning
Get started with Automated Machine Learning using Azure
Explore aspects such as algorithm selection, auto featurization, and hyperparameter tuning
Understand how data analysts, BI professionals, and developers can use Automated Machine Learning in their familiar tools and experiences
Learn how to get started using Automated Machine Learning for use cases including classification and regression.
Sprache
Verlagsort
Produkt-Hinweis
Broschur/Paperback
Klebebindung
Maße
Höhe: 233 mm
Breite: 178 mm
Dicke: 11 mm
Gewicht
ISBN-13
978-1-4920-5559-4 (9781492055594)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Deepak Mukunthu is a product leader with 16+ years of experience. With his experience in Big data, Analytics and AI, Deepak has played instrumental leadership roles in transforming organizations and teams become data driven and adopt machine learning. He brings a good mix of thought leadership, customer understanding and innovation to design and deliver compelling products that resonate well with customers. In his current role of Principal Program Manager on Automated ML in Azure AI platform group at Microsoft, Deepak drives product strategy and roadmap for Automated ML with the goal of accelerating AI for data scientists and democratizing AI for other personas interested in machine learning. In addition to shaping the product direction, he also plays an instrumental role in helping customers adopt Automated ML for their business-critical scenarios. Prior to joining Microsoft, Deepak worked at Trilogy where he played multiple roles - Consultant, Business development, Program manager, Engineering manager - successfully leading distributed teams across the globe and managing technical integration of acquisitions.
Parashar Shah works for Microsoft as a Data Scientist, Senior Program/Product Manager in Azure Machine Learning platform team within the Cloud + AI Platform organization. His first book, Hands-On Machine Learning with Azure: Build powerful models with cognitive machine learning and artificial intelligence, was published in Nov 2018. Prior to joining Microsoft, he worked for Alcatel-Lucent/Nokia Networks/Bell Labs where he helped global telecom operators (across North America, Europe, Middle East and APAC) as a solution architect/product manager. Parashar has a MBA from Indian Institute of Management Bangalore & B.E. (E.C.) from Nirma Institute of Technology, Ahmedabad. He has filed for 5 patents (in published state), he loves to work on new technologies and ideas. Parashar's experience and interests span across Artificial Intelligence, Machine Learning, Big Data, Data Science, Blockchain, Virtual Reality, Internet of Things (IoT), Advanced Analytics, Mobile application development, Wireless Technologies & Device Management.
Wee Hyong Tok is part of the AzureCAT team at Microsoft. He has extensive leadership experience leading multi-disciplinary team of engineers and data scientists, working on cutting-edge AI capabilities that are infused into products and services. He is a tech visionary with a background in product management, machine learning/deep learning and working on complex engagements with customers. Over the years, he has demonstrated that his early thought-leadership white papers on tech trends have become reality, and deeply integrated into many products. His ability to strategize, and turn strategy to execution, and hunting for customer adoption has enabled many projects that he works on to be successful. He is continuously pushing the boundaries of products for machine learning and deep learning. His team works extensively with deep learning frameworks, ranging from TensorFlow, CNTK, Keras, and PyTorch. Wee Hyong has worn many hats in his career - developer, program/product manager, data scientist, researcher, and strategist, and his range of experience has given him unique super powers to lead and define the strategy for high-performing Data and AI innovation teams. Throughout his career, he has been a trusted advisor to the C-suite, from Fortune 500 companies to startups.
