SQL Server Machine Learning Services: Python

SQL Server Machine Learning Services: Python
SQL Server Machine Learning Services: Python
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 28m | 381 MB

Learn how to analyze SQL Server data with Python. Database expert Adam Wilbert shows how to use a powerful combination of tools, including high-performance Python libraries and the Machine Learning Services add-on, directly inside SQL Server to streamline analysis. Adam shows how to use Python scripts to perform statistical analysis, generate graphics such as scatterplots and bar charts, and process tabular data. He also explains how to turn a Python script into a stored procedure and set up standalone ML services to execute scripts without impacting SQL Server performance.

Topics include:

  • Analyzing SQL Server data with Python
  • Installing Machine Learning Services
  • Writing Python scripts for SQL Server
  • Python packages and libraries
  • Producing graphics with Matplotlib
  • Processing tabular data
  • Creating a SQL stored procedure
  • Creating an external data science client
Table of Contents

1 Analyze SQL Server data with Python
2 What you should know
3 Using the exercise files
4 What is machine learning services
5 Install ML services for Python
6 Enable script execution in SQL Server
7 Use variables in Python
8 Create a Python while loop
9 Import a dataset from SQL Server
10 Manipulate a data frame
11 Output a result set to SQL Server
12 Python syntax pitfalls
13 Challenge Import a data frame
14 Solution Import a data frame
15 The Anaconda open-source packages
16 Functions in the revoscalepy package
17 Model, train, and score with microsoftml
18 Produce graphics with MatPlotLib
19 Get descriptive statistics with pandas
20 Challenge Sample a data frame
21 Solution Sample a data frame
22 Return values with indexes and series
23 Convert a series to a data frame
24 Add multiple series to a data frame
25 Include the index in a data frame
26 Slice a data frame to series
27 Challenge Import and process data
28 Solution Import and process data
29 Create a Python stored procedure
30 Parameterize the procedure
31 Challenge Write a stored procedure
32 Solution Write a stored procedure
33 Install MLS on a standalone server
34 Add development tools to the client
35 Work with Jupyter Notebooks
36 Next steps