Course info
Dec 13, 2019
1h 40m

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.

About the author
About the author

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.

Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi everyone. My name is Bismark Adomako, and welcome to my course, Building, Training, and Validating Models in Microsoft Azure. I'm a data engineer at Ecobank eProcess International S.A. This course gives Microsoft Azure data scientists a road map on how to build, train, and validate machine learning models in Azure. The course will try to predict if a flight will be delayed more than 15 minutes with the given data. Some of the major topics that will be covered includes creating a hypothesis, identifying features from raw data, building a model, and then monitoring and managing the model performance. By the end of this course, you will know how to use Microsoft Azure Machine Learning services to build, train, and validate models. Before beginning the course, you should be familiar with intermediate level of understanding data science concepts such as supervised classification. I hope you join me on this journey to learn more about Azure Machine Learning services with the Building, Training, and Validating Models in Microsoft Azure course, at Pluralsight.