課程大綱：物聯(lián)網(wǎng)解決方案的預(yù)測(cè)分析培訓(xùn)
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.
Lab 1: Examining Machine Learning for IoT
Lab 2: Getting Started with Azure Machine Learning
Lab 3: Exploring Code-First Machine Learning with PythonModule
2: Data Preparation for Predictive Maintenance ModelingLab 1: Exploring IoT Data with Python
Lab 2: Cleaning and Standardizing IoT Data
Lab 3: Applying Advanced Data Exploration TechniquesModule
3: Feature Engineering for Predictive Maintenance ModelingLab 1: Exploring Feature Engineering
Lab 2: Applying Feature Selection TechniquesModule 4: Fault PredictionLab 1: Training a Predictive Model
Lab 2: Analyzing Model Performance
The lab outline is provided below.Module 1: Introduction to Machine Learning for IoT
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