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.
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
Basic understanding of data and python notebooks
In a browser, and if you do not already have a free Azure Notebooks account, go to https://notebooks.azure.com and sign up for one using your Microsoft account.
If you do not already have a Microsoft account, go to https://signup.live.com and create one.
Navigate to https://notebooks.azure.com/JonJordanBI/projects/predictiveanalyticsforiot 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
03b- Feature Selection.ipynb
04a- Train Predictive Model.ipynb
03b- Analyze Model Performance.ipynb
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.