Predictive Analytics & IoT Solutions with Microsoft Azure Notebooks

UST Room #419

3:30 pm - 5:30 pm

Data & Machine Learning

How This Fits Into IoT
Using machine learning helps users develop a deeper understanding of their IoT data.

What Attendees Do
Participate in a series of hands-on lab activities guiding them through a series of machine learning tasks common for IoT scenarios.  This particular scenario will focus on predictive maintenance.

Learning Objectives
Prepare data for machine learning operations; apply feature engineering as part of the analysis process, choose the appropriate machine learning algorithm for the appropriate business scenario; train, evaluate and apply regression models; evaluate the effectiveness of regression models

What Attendees Bring

Attendee Preparation Work (Downloads, Reading)
Complete the Pre-class set-up

Knowledge Required
Basic understanding of data and python notebooks

Pre-class Set-up
In a browser, and if you do not already have a free Azure Notebooks account, go to and sign up for one using your Microsoft account.
If you do not already have a Microsoft account, go to and create one.
Navigate to and clone Predictive Analytics for IoT Solutions.  Ensure the following are now located in your environment:
02a-Explore IoT Data with Python.ipynb
02b-Clean and Standardize Data.ipynb
02c-Advanced Data Exploration Techniques.ipynb
03a-Feature Engineering.ipynb
03b- Feature Selection.ipynb
04a- Train Predictive Model.ipynb
03b- Analyze Model Performance.ipynb
Output folder
Source Data folder

What Attendees Receive
Basic understanding of steps required to evaluate IoT data and apply machine learning regression models. Students will take with them a series of Jupyter notebooks that can be used as a starting point in the future to apply analytics to their own IoT data.

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