Course Summary
Learn how to plan and implement Industrial IoT projects using machine learning with this Coursera course. Discover how to integrate sensors and data analytics into your automation systems, and explore the potential applications of IoT in various industries.Key Learning Points
- Explore the fundamentals of Industrial IoT and its potential applications in different industries
- Learn how to integrate sensors and data analytics into your automation systems
- Discover how machine learning can be used to improve the efficiency and accuracy of your IoT projects
Related Topics for further study
Learning Outcomes
- Ability to plan and execute Industrial IoT projects using machine learning techniques
- Understanding of the potential applications of IoT in various industries
- Skills to integrate sensors and data analytics into automation systems
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of IoT concepts and techniques
- Familiarity with programming languages such as Python and R
Course Difficulty Level
IntermediateCourse Format
- Online Self-Paced Course
- Video Lectures
- Quizzes and Assignments
Similar Courses
- IoT Architecture
- Internet of Things (IoT)
- IoT Sensors and Devices
Related Education Paths
- Professional Certificate in Industrial IoT
- Professional Certificate in Data Science
- Professional Certificate in Machine Learning
Notable People in This Field
- Vint Cerf
- Sudha Jamthe
Related Books
Description
This course can also be taken for academic credit as ECEA 5386, part of CU Boulder’s Master of Science in Electrical Engineering degree.
Knowledge
- How to staff, plan and execute a project.
- How to build a bill of materials for a product.
- How to calibrate sensors and validate sensor measurements.
- How hard drives and solid state drives operate.
Outline
- Project Planning and Staffing
- Introduction
- Segment 1 - Learning Outcomes, Introduction to a Design Process
- Segment 2 - Requirements, Scope, Schedule, Resources, Heap Chart
- Segment 3 - Roles and Responsibilities
- Segment 4 - Process: Architecture Definition, Design Planning
- Segment 5 - Process: Architecture Definition, Design Planning 2
- Segment 6 - Process: Develop
- Segment 7 - Process: Verification
- Segment 8 - Process: Manufacture
- Segment 9 - Process: Deploy
- Segment 10 - Process: Validation
- Segment 11 - Temperature
- Access to Course Resources
- A Note from the Instructor
- Module 1 Quiz
- Sensors and File Systems
- Introduction
- Segment 1 - Learning Outcomes, Introduction to Thermistors
- Segment 2 - Terminology: Resolution, Precision, Accuracy, Tolerance
- Segment 3 - Basic Sensor Circuit
- Segment 4 - Accuracy Example
- Segment 5 - Calculating Rtherm
- Segment 6 - Validating Calibration
- Segment 7 - Filtering Techniques
- Segment 8 - Block, Object and Key-Value Storage Devices
- Segment 9 - Filesystem Basics
- Segment 10 - A File on a Hard Drive
- Segment 11 - A File on a Solid State Drive
- Segment 12 - File System: NFS
- Segment 13 - How Big is "Big"?
- Segment 14 - Traditional File System Bottlenecks
- Segment 15 - Parallel Distributed File Systems: Hadoop, Lustre
- Module 2 Quiz
- Machine Learning
- Introduction
- Segment 1 - Learning Outcomes
- Segment 2 - AI Backgrounder
- Segment 3 - Machine Learning, What is it?
- Segment 4 - Machine Learning Schools of Thought
- Segment 5 - Get the Tools
- Segment 6 - Categories of Machine Learning
- Segment 7 - Supervised Learning, Linear Regression 1
- Segment 8 - Supervised Learning, Linear Regression 2
- Segment 9 - Supervised Learning, Linear Regression 3
- Segment 10 - Supervised Learning, Linear Regression 4
- Segment 11 - Supervised Learning, Bayes Theorem
- Segment 12 - Supervised Learning, Naive Bayes
- Segment 13 - Supervised Learning, Support Vector Machines (SVM) Introduction
- Segment 14 - Supervised Learning, SVMs
- Segment 15 - Unsupervised Learning, K-Means
- Segment 16 - Reinforcement Learning
- Segment 17 - Supervised Learning, Deep Learning
- Segment 18 - Rick Rashid, Natural Language Processing
- Segment 19 - Deep Learning, Hearing Aid
- Segment 20 - Machine Learning in IIoT
- Segment 21 - Machine Learning Summary
- Module 3 Quiz
- Big Data Analytics
- Introduction
- Segment 1 - Learning Outcomes, Definition of Big Data
- Segment 2 - Importance of Big Data, Characteristics of Big Data
- Segment 3 - Size of Big Data
- Segment 4 - Introduction to Predictive Analytics
- Segment 5 - Role of Statistics and Data Mining
- Segment 6 - Machine Learning, Generalization and Discrimination
- Segment 7 - Frameworks, Testing and Validating
- Segment 8 - Bias and Variance in your Data
- Segment 9 - Out-of-sample Data and Learning Curves
- Segment 10 - Cross Validation
- Segment 11 - Model Complexity, Over- and Under-fitting
- Segment 12 - Processing Your Data Prior to Machine Learning
- Segment 13 - Good Data, Smart Data
- Segment 14 - Visualizing Your Data
- Segment 15 - Principal Component Analysis (PCA)
- Segment 16 - Prognostic Health Management, Hadoop Machine Learning Library
- Segment 17 - My Example: Predicting NFL Football Winners
- Segment 18 - Tom Bradicich, Hewlett Packard's Viewpoint on Big Data
- Module 4 Quiz
Summary of User Reviews
This course on Industrial IoT Project Planning and Machine Learning is highly recommended by reviewers. The course covers a wide range of topics and is taught by experienced instructors. Many users found the hands-on projects to be particularly helpful.Key Aspect Users Liked About This Course
Hands-on projectsPros from User Reviews
- Experienced instructors
- Wide range of topics covered
- Hands-on projects are helpful
- Clear and concise explanations
- Good community support
Cons from User Reviews
- Some users found the course to be too technical
- Course material can be overwhelming at times
- Content is not suitable for beginners
- Some users had issues with the platform
- No certificate of completion offered for free
David Sluiter
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