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Predictive Analytics for IoT Solutions


Microsoft

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

你是否已准备好开始使用机器学习,来更深入地了解你的物联网数据?

本课程使用动手实验室活动来指导学生完成物联网场景中常见的一系列机器学习实现,例如预测性维护。完成本课程后,学生将能够使用他们的物联网数据实施预测分析。

该课程分为四个模块,涵盖以下主题领域:

  • 物联网的机器学习
  • 数据准备技术
  • 预测性维护建模
  • 故障预测建模

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
  • 描述常见的与物联网相关的机器学习场景和算法
  • 说明如何使用物联网解决方案加速器进行预测性维护
  • 准备机器学习操作和分析的数据
  • 在分析过程中应用特征工程
  • 为给定的业务场景选择合适的机器学习算法
  • 根据机器学习算法的类型识别目标变量
  • 训练,评估和应用各种回归模型
  • 评估回归模型的有效性
  • 将深度学习应用于预测性维护方案

Prerequisites

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

在开始本课程之前,学生应该了解以下内容

  • 物联网术语和业务目标
  • 如何使用现代软件开发工具
  • Python 编程的基本原理
  • 基本数据分析技术
  • 一般机器学习概念

Course Syllabus

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

本节课全部使用实验教学,不包含任何讲座或阅读内容。所有学习内容都直接嵌入在课程实验中,并已经根据学习进度设计安排顺序,包括对工具和技术的介绍,参考以及额外内容,视频展示预计代码解释。

测试问题也将出现在学习活动中,这些练习问题将帮助你准备期末考试。

本节课包含四个单元,每个单元包含至少两个实验练习活动。实验提纲如下。

第1单元:物联网机器学习简介

  • 实验1:检查物联网的机器学习
  • 实验2:Azure 机器学习入门
  • 实验3:使用 Python 探索代码优先的机器学习

第2单元:预测性维护建模的数据准备

  • 实验1:使用 Python 探索物联网数据
  • 实验2:清理和正则化物联网数据
  • 实验3:应用高级数据探索技术

第3单元:预测性维护建模的特征工程

  • 实验1:探索特征工程
  • 实验2:应用特征选择技术

第4单元:故障预测

  • 实验1:培训预测模型
  • 实验2:分析模型性能

Course Staff

Chris_Howd.png

Chris Howd

Engineer and Software Developer

Microsoft

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.

Sheila_Shahpari.png

Sheila Shahpari

CTO, Paritta Group

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.

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