This chapter introduced the most popular unsupervised data-mining and machine-learning methods, including the association rules, the different types of clustering, the principal-component analysis, and the factor analysis. In addition, you learned how to add either R or Python packages to the ML services (In-Database). After warming up in the first three chapters, the complexity of the work and the analyses have increased. I have left the supervised methods, which are used for probably the most exciting part—predictive analytics—for the last chapter of this book.
Data Science with SQL Server Quick Start Guide
By :
Data Science with SQL Server Quick Start Guide
By:
Overview of this book
SQL Server only started to fully support data science with its two most recent editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you.
This book is the ideal introduction to data science with Microsoft SQL Server and In-Database ML Services. It covers all stages of a data science project, from businessand data understanding,through data overview, data preparation, modeling and using algorithms, model evaluation, and deployment.
You will learn to use the engines and languages that come with SQL Server, including ML Services with R and Python languages and Transact-SQL. You will also learn how to choose which algorithm to use for which task, and learn the working of each algorithm.
Table of Contents (15 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Free Chapter
Writing Queries with T-SQL
Introducing R
Getting Familiar with Python
Data Overview
Data Preparation
Intermediate Statistics and Graphs
Unsupervised Machine Learning
Supervised Machine Learning
Other Books You May Enjoy
Index
Customer Reviews