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Building, Training, and Validating Models in Microsoft Azure

via Pluralsight

Overview

This course gives Microsoft Azure Data Scientists a road map on how to build, train, and validate machine learning models in Azure.

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.

Topics:
  • Course Overview
  • Creating a Hypothesis
  • Sourcing and Transforming Data Relevant to a Hypothesis
  • Identifying Features from Raw Data
  • Building the Model
  • Monitoring and Managing the Performance of a Model

Taught by

Bismark Adomako

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