Machine Learning with Microsoft Technologies: Selecting the Right Architecture and Tools for Your Project - Brossura
Sinossi
Part I: Getting Started
Chapter 1: Introduction to Machine Learning
Chapter 2: Introduction to R
Chapter 3: Introduction to Python
Chapter 4: R Visualization in Power BI
Part II: Machine Learning with R and Power BI
Chapter 5: Business Understanding
Chapter 6: Data Wrangling for Predictive Analysis
Chapter 7: Predictive Analysis in Power Query with R
Chapter 8: Descriptive Analysis in Power Query with R
Part III: Machine Learning SQL Server
Chapter 9: Using R with SQL Server 2016 and 2017
Part IV: Machine Learning in Azure
Chapter 10: Azure DataBricks
Chapter 11: R in Azure Data Lake
Chapter 12: Azure Machine Learning Studio
Chapter 13: Machine Learning in Azure Stream Analytics
Chapter 14: Azure Machine Learning (ML) Workbench
Chapter 15: Machine Learning on HDInsight
Chapter 16: Data Science Virtual Machine and AI Framework
Chapter 17: Deep Learning Tools with Cognitive Toolkit (CNTK)
Part V: Data Science Virtual Machine
Chapter 18: Cognitive Service Toolkit
Chapter 19: Bot Framework
Chapter 20: Overview on Microsoft Machine Learning Tools
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
