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SQL Server 2017 Machine Learning Services with R: Data exploration, modeling, and advanced analytics (English Edition)
Free Download SQL Server 2017 Machine Learning Services with R: Data exploration, modeling, and advanced analytics (English Edition)
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Über den Autor und weitere Mitwirkende
Tomaz Kastrun is a SQL Server developer and data scientist with more than 15 years of experience in the fields of business warehousing, development, ETL, database administration, and query tuning. He holds over 15 years of experience in data analysis, data mining, statistical research, and machine learning. He is a Microsoft SQL Server MVP for data platform and has been working with Microsoft SQL Server since version 2000. He is a blogger, author of many articles, a frequent speaker at the community and Microsoft events. He is an avid coffee drinker who is passionate about fixed gear bikes. Julie Koesmarno Julie Koesmarno is a senior program manager in the Database Systems Business Analytics team, at Microsoft. Currently, she leads big data analytics initiatives, driving business growth and customer success for SQL Server and Azure Data businesses. She has over 10 years of experience in data management, data warehousing, and analytics for multimillion-dollar businesses as a SQL Server developer, a system analyst, and a consultant prior to joining Microsoft. She is passionate about empowering data professionals to drive impacts for customer success and business through insights.
Produktinformation
Taschenbuch: 338 Seiten
Verlag: Packt Publishing (27. Februar 2018)
Sprache: Englisch
ISBN-10: 1787283577
ISBN-13: 978-1787283572
Größe und/oder Gewicht:
19 x 2 x 23,5 cm
Durchschnittliche Kundenbewertung:
Schreiben Sie die erste Bewertung
Amazon Bestseller-Rang:
Nr. 270.888 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
I am currently working on the Data Science professional program by Microsoft and am taking the Analyzing Big Data with Microsoft R for one of the course options (note: this book isn't required for the class--I just like having reference books that supplement what I'm learning). I've bought several R programming books but this is the first and so far, only, book I've been able to find about SQL Server Machine Learning Services with R.I've read through 5 chapters already and I wish I had it months ago when I was getting started programming in R because I had issues here and there with my machine set-up and the book covered some of the problems I had.If you're new to R programming and machine learning--I'd recommend reading other books that go more into detail about the specifics of the R language, data preparation, modeling, etc. This book doesn't go into the R programming language enough for someone new to the subject to get a handle of the kinds of objects in R, what you can and cannot do with each, etc. Nor does it go into the differences of the different types of regression, how to interpret data to determine features to include/not include in your model, etc.The book does a great job of going through everything in the machine learning process in a SQL Server environment from setup to deployment. I'm glad I've got it on my desk.
Many of the code examples in the book will not run. I'm using SQL Server 2017. Also, in almost all cases the author states the high level purpose of some code, presents the code, but gives little or no explanation of how the code works. I'm very disappointed.
I am one of the program managers who worked with the release of this feature in SQL Server. (I received a copy this book from the author in exchange for my honest opinion). This book does an excellent job at providing an end-to-end guide to SQL Server Machine Learning Services, covering the feature in the context of the full data science process. It targets the different roles involved in a successful deployment of a Machine Learning project and contains very useful content for each of those roles. The roles I am referring to are Data Scientist, data engineer and database administrator. Another thing that makes this book practically very useful is that the authors provide the code files covered per chapter. This makes this book a great training material for anyone who wants to thoroughly learn how to leverage R and Python in SQL Server.
I got this book for free for my honest review. I agree with other reviewers on the pros of this book. Unlike other general R books, this is specifically designed for SQL Server. The usage of this book is straightforward. The book is written in a smooth way in terms of context.This book is not beneficial for someone who wants to learn R or statistical learning. For example, the definition of NOVA is not given but how to use it is illustrated in the book in terms of coding as well as the result with sample data!However, I feel this is written in a hurry. There are lots of formatting issues that editor forgot to correct. Lots of figures have no title,... and the text and script of codes are brought together without proper spacing...
I am one of the Microsoft developers that contributed to some features described in this book (I got this book from the author, for free, but that doesn't have an effect on my review). Therefore, before reading this book, I didn’t expect to see much I didn't already know. However, I was pleasantly surprised. This book not only tells a complete story – which starts from basic things such as installation, user creation, permission setting and goes all the way to extended features such as Polybase – but also comes with a lot of practical examples that can be utilized in various scenarios. To me that’s the most valuable part, as I am not a data scientist (I did study R for my work but never solved any real problems with it) and I am happy to see that using our product people can do so many things with their data! The book also introduces functionalities from different Microsoft products which help the data scientists on machine learning/data mining tasks (some of them I only heard of and never used myself, and it is very helpful to see detailed instructions and screen shots). Following this book step by step, one can easily learn how to use all those tools and mine their data. I especially like the organization of the chapters – from basic to advanced, each chapter clearly defines prerequisites, provides instructions and examples and gives precise comments. IMHO this book is a good reference for people trying to create their data science solutions.
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