Predictive Analytics for IoT Solutions
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About this course
Learn how to apply machine learning to your IoT data and gain a valuable advantage over your business competition. This course provides hands-on experience developing predictive maintenance and other ML solutions for IoT scenarios.
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
- 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
What you will learn
After completing this course, students will be able to:
- 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
Estimate Time : 8-12 hours
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
Senior Content Developer Microsoft
Chris Howd is a senior content developer at Microsoft who focuses on creating training products for the developer audience. Chris started working at Microsoft in 1999, just before the first .NET Framework products were released as beta previews, and has been involved in supporting the launch of .NET products ever since. Most recently, Chris has been working on developer training that supports Microsoft Azure PaaS, Azure IoT solutions, Windows 10 UWP, and Windows 10 IoT Core.
MS Business Application MVP
Sheila Shahpari is a software engineer and Microsoft Business Applications MVP. She has served in many roles within the professional services industry, starting her career as a programmer and software development consultant. She now serves as the Chief Technology Officer for Paritta Group, a digital agency specializing in Customer Engagement and Augmented Intelligence solutions. Her prior experience includes roles in learning and development leadership, consulting as a technical architect, and practice leadership for a regional Microsoft Partner. Sheila also provides technical guidance and services to start-ups in the AI and business application space. She specializes in Enterprise Architecture, Delivery Leadership, Capability Development, CRM, Enterprise Search, AI, and Software Development.
Daren May is the President and founder of CustomMayd, a company that specializes in designing and building developer training and next-level digital experiences. Daren is a Windows Development MVP and has written and presented many training courses on a variety of developer topics. His courses can be found on Microsoft Virtual Academy, Channel9 and EdX.org.
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