Explore predictive analytics using step-by-step tutorials and build models to make prediction in a jiffy with a few mouse clicks
The book is intended for those who want to learn how to use Azure Machine Learning. Perhaps you already know a bit about Machine Learning, but have never used ML Studio in Azure; or perhaps you are an absolute newbie. In either case, this book will get you up-and-running quickly.
This book provides you with the skills necessary to get started with Azure Machine Learning to build predictive models as quickly as possible, in a very intuitive way, whether you are completely new to predictive analysis or an existing practitioner.
The book starts by exploring ML Studio, the browser-based development environment, and explores the first step―data exploration and visualization. You will then build different predictive models using both supervised and unsupervised algorithms, including a simple recommender system. The focus then shifts to learning how to deploy a model to production and publishing it as an API.
The book ends with a couple of case studies using all the concepts and skills you have learned throughout the book to solve real-world problems.
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The book is intended for those who want to learn how to use Azure Machine Learning. Perhaps you already know a bit about Machine Learning, but have never used ML Studio in Azure; or perhaps you are an absolute newbie. In either case, this book will get you up-and-running quickly.
Sumit Mund is a BI/analytics consultant with about a decade of industry experience. He works in his own company, Mund Consulting Ltd, where he is a director and lead consultant. He is an expert in machine learning, predictive analytics, C#, R, and Python programming; he also has an active interest in Artificial Intelligence. He has extensive experience working with most of Microsoft Data Analytics tools and also on Big Data platforms, such as Hadoop and Spark. He is a Microsoft Certified Solution Expert (MCSE in Business Intelligence). Sumit regularly engages on social media platforms through his tweets, blogs, and LinkedIn profile, and often gives talks at industry conferences and local user group meetings.
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