Develop ML Models with Python and T-SQL
Develop ML Models with Python and T-SQL
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 39m | 100 MB
Instructor: Sam Nasr
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 39m | 100 MB
Instructor: Sam Nasr
In this course, senior software engineer and instructor Sam Nasr guides you through the skills you need to leverage Python and T-SQL for building robust machine learning models. Discover how to set up your development environment, import data seamlessly into SQL Server, and prepare it for model training. Explore the fundamentals of various machine learning algorithms, data pre-processing techniques, and tuning hyperparameters for optimal performance.
Learn how to use tools such as Visual Studio Code, Pandas, SQL Alchemy, and scikit-learn for effective model development and deployment. Plus, find out how to store, access, and manage version history of your trained models directly within your SQL Server database. By the end of the course, you will be equipped to train, evaluate, and deploy machine learning models adeptly, and implement best practices for data handling and model management.
Learning objectives
- Identify how to configure SQL Server to use Python code in T-SQL scripts.
- Articulate how to ttrain, evaluate, and use a ML model to make predictions using Python and T-SQL.
- Understand how to use Python libraries to train a ML model using data in a SQL Server database using Python and T-SQL.
- Determine how to store and retrieve a functional ML model in a SQL Server database using Python and T-SQL.
Develop ML Models with Python and T-SQL
