About This Course
Are you ready to start using machine learning to develop a deeper understanding of your IoT data?
This course uses hands-on lab activities to guide students through a series of machine learning implementations that are common for IoT scenarios, such as predictive maintenance. After completing this course, students will be able to implement predictive analytics using their IoT data.
The course is divided into four modules that cover the following topic areas:
- Machine learning for IoT
- Data preparation techniques
- Predictive maintenance modeling
- Fault prediction modeling
Please Note: Learners who successfully complete this course can earn a CloudSwyft digital certificate and skill badge - these are detailed, secure and blockchain authenticated credentials that profile the knowledge and skills you’ve acquired in this course.
What you'll learn
- Describe machine learning scenarios and algorithms commonly pertinent to IoT
- Explain how to use the IoT solution Accelerator for Predictive Maintenance
- Prepare data for machine learning operations and analysis
- Apply feature engineering within the analysis process
- Choose the appropriate machine learning algorithms for given business scenarios
- Identify target variables based on the type of machine learning algorithm
- Train, evaluate, and apply various regression models
- Evaluate the effectiveness of regression models
- Apply deep learning to a predictive maintenance scenario
Before starting this course, students should understand the following:
- IoT terminology and business goals
- How to use modern software development tools
- Basic principles of Python programming
- Basic data analytics techniques
- General machine learning concepts
This course is completely lab-based. There are no lectures or required reading sections. All of the learning content that you will need is embedded directly into the labs, right where and when you need it. Introductions to tools and technologies, references to additional content, video demonstrations, and code explanations are all built into the labs.
Some assessment questions will be presented during the labs. These questions will help you to prepare for the final assessment.
The course includes four modules, each of which contains two or more lab activities. The lab outline is provided below.
Module 1: Introduction to Machine Learning for IoT
- Lab 1: Examining Machine Learning for IoT
- Lab 2: Getting Started with Azure Machine Learning
- Lab 3: Exploring Code-First Machine Learning with Python
Module 2: Data Preparation for Predictive Maintenance Modeling
- Lab 1: Exploring IoT Data with Python
- Lab 2: Cleaning and Standardizing IoT Data
- Lab 3: Applying Advanced Data Exploration Techniques
Module 3: Feature Engineering for Predictive Maintenance Modeling
- Lab 1: Exploring Feature Engineering
- Lab 2: Applying Feature Selection Techniques
Module 4: Fault Prediction
- Lab 1: Training a Predictive Model
- Lab 2: Analyzing Model Performance
Engineer and Software Developer
Chris is an engineer and software developer who has been working at Microsoft in various roles for the past 15 years. Before coming to Microsoft, Chris worked for the U.S. Department of Defense designing and developing computer controlled instrumentation and robotic systems, and was a self-employed contractor doing engineering research with NASA and select engineering start-ups.
CTO, Paritta Group
Frequently Asked Questions
Who can take this course?
Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.