- Course
Building, Training, and Validating Models in Microsoft Azure
This course gives Microsoft Azure Data Scientists a road map on how to build, train, and validate machine learning models in Azure.
Advanced
- Course
Building, Training, and Validating Models in Microsoft Azure
This course gives Microsoft Azure Data Scientists a road map on how to build, train, and validate machine learning models in Azure.
Advanced
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This course is included in the libraries shown below:
- AI
- Cloud
- Data
What you'll learn
Building machine learning models in Microsoft Azure can appear intimidiating. This course, Building, Training, and Validating Models in Microsoft Azure, will help you decide which model to choose and why by building a model which will try to predict if a flight would be delayed more than 15 mins with given data. First, you will go through a real world problem to see how Azure ML can solve this problem, helping you form a hypothesis on which the model performance can be judged.
Next, you will quickly get Azure ML set up and learn why you need to split data for training and testing the models.
Then, you will explore the dependent and independent variables, which independent variables should be picked, why they should be picked, as well as feature data conversion such as label encoding and feature scaling.
Finally, you will discover which models to choose and why before obtaining the score of the model which will show how we can optimize the model and re-test.
When you are finished with this course, you will be ready to put your own model into production and monitor and retrain that model when necessary.
Building, Training, and Validating Models in Microsoft Azure
Advanced
Bismark is a BI & Big Data Engineer obsessed with applying his knowledge in computer engineering and mathematics in the fields of Data Science, Artificial Intelligence, Machine Learning, Big Data, and Human Computer Interaction to find disease cures, provision of better healthcare and technology, autonomous systems, education and productivity through research into novel methods and algorithms for computation.