This course is part of the Developing Industrial Internet of Things Specialization

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David Sluiter

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Part of the Master of Science in Electrical Engineering degree
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Developing Industrial Internet of Things SpecializationUniversity of Colorado Boulder

About this Course

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This course can also be taken for academic credit as ECEA 5386, part of CU Boulder’s Master of Science in Electrical Engineering degree.
This is part 2 of the specialization. In this course students will learn :
  * 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
  * How basic file systems operate, and types of file systems used to store big data
  * How machine learning algorithms work - a basic introduction
  * Why we want to study big data and how to prepare data for machine learning algorithms

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

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Course 2 of 3 in the

Developing Industrial Internet of Things Specialization

Intermediate Level

Approx. 17 hours to complete

English

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What you will learn

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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 2 of 3 in the

Developing Industrial Internet of Things Specialization

Intermediate Level

Approx. 17 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.72/5 (24 Ratings)

David Sluiter

Professor Adjunct

Electrical, Computer, and Energy Engineering

28,974 Learners

4 Courses

Offered by

University of Colorado Boulder

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Electrical Engineering from University of Colorado Boulder. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Week1

Week 1

10 hours to complete

Project Planning and Staffing

10 hours to complete

12 videos (Total 112 min), 2 readings, 2 quizzes

12 videos

Introduction1mSegment 1 - Learning Outcomes, Introduction to a Design Process12mSegment 2 - Requirements, Scope, Schedule, Resources, Heap Chart15mSegment 3 - Roles and Responsibilities6mSegment 4 - Process: Architecture Definition, Design Planning13mSegment 5 - Process: Architecture Definition, Design Planning 218mSegment 6 - Process: Develop9mSegment 7 - Process: Verification11mSegment 8 - Process: Manufacture2mSegment 9 - Process: Deploy10mSegment 10 - Process: Validation6mSegment 11 - Temperature5m

2 readings

Access to Course Resources10mA Note from the Instructor5m

1 practice exercise

Module 1 Quiz10m

Week2

Week 2

2 hours to complete

Sensors and File Systems

2 hours to complete

16 videos (Total 103 min)

16 videos

Introduction28sSegment 1 - Learning Outcomes, Introduction to Thermistors3mSegment 2 - Terminology: Resolution, Precision, Accuracy, Tolerance6mSegment 3 - Basic Sensor Circuit5mSegment 4 - Accuracy Example2mSegment 5 - Calculating Rtherm2mSegment 6 - Validating Calibration5mSegment 7 - Filtering Techniques11mSegment 8 - Block, Object and Key-Value Storage Devices15mSegment 9 - Filesystem Basics3mSegment 10 - A File on a Hard Drive5mSegment 11 - A File on a Solid State Drive8mSegment 12 - File System: NFS4mSegment 13 - How Big is "Big"?8mSegment 14 - Traditional File System Bottlenecks3mSegment 15 - Parallel Distributed File Systems: Hadoop, Lustre13m

1 practice exercise

Module 2 Quiz18m

Week3

Week 3

3 hours to complete

Machine Learning

3 hours to complete

22 videos (Total 132 min)

22 videos

Introduction30sSegment 1 - Learning Outcomes1mSegment 2 - AI Backgrounder6mSegment 3 - Machine Learning, What is it?6mSegment 4 - Machine Learning Schools of Thought9mSegment 5 - Get the Tools3mSegment 6 - Categories of Machine Learning5mSegment 7 - Supervised Learning, Linear Regression 17mSegment 8 - Supervised Learning, Linear Regression 29mSegment 9 - Supervised Learning, Linear Regression 38mSegment 10 - Supervised Learning, Linear Regression 49mSegment 11 - Supervised Learning, Bayes Theorem4mSegment 12 - Supervised Learning, Naive Bayes9mSegment 13 - Supervised Learning, Support Vector Machines (SVM) Introduction55sSegment 14 - Supervised Learning, SVMs12mSegment 15 - Unsupervised Learning, K-Means11mSegment 16 - Reinforcement Learning46sSegment 17 - Supervised Learning, Deep Learning2mSegment 18 - Rick Rashid, Natural Language Processing8mSegment 19 - Deep Learning, Hearing Aid2mSegment 20 - Machine Learning in IIoT4mSegment 21 - Machine Learning Summary4m

1 practice exercise

Module 3 Quiz22m

Week4

Week 4

2 hours to complete

Big Data Analytics

2 hours to complete

19 videos (Total 119 min)

19 videos

Introduction33sSegment 1 - Learning Outcomes, Definition of Big Data3mSegment 2 - Importance of Big Data, Characteristics of Big Data4mSegment 3 - Size of Big Data4mSegment 4 - Introduction to Predictive Analytics2mSegment 5 - Role of Statistics and Data Mining3mSegment 6 - Machine Learning, Generalization and Discrimination7mSegment 7 - Frameworks, Testing and Validating5mSegment 8 - Bias and Variance in your Data3mSegment 9 - Out-of-sample Data and Learning Curves5mSegment 10 - Cross Validation5mSegment 11 - Model Complexity, Over- and Under-fitting3mSegment 12 - Processing Your Data Prior to Machine Learning8mSegment 13 - Good Data, Smart Data6mSegment 14 - Visualizing Your Data1mSegment 15 - Principal Component Analysis (PCA)2mSegment 16 - Prognostic Health Management, Hadoop Machine Learning Library11mSegment 17 - My Example: Predicting NFL Football Winners18mSegment 18 - Tom Bradicich, Hewlett Packard's Viewpoint on Big Data20m

1 practice exercise

Module 4 Quiz26m

Reviews

4.6

22 reviews

5 stars
76.84%
4 stars
14.73%
3 stars
4.21%
2 stars
2.10%
1 star
2.10%

TOP REVIEWS FROM PROJECT PLANNING AND MACHINE LEARNING

by MFeb 26, 2022

Brilliant course! A must for all the budding IoT professionals. Thanks Prof David, The University of Colorado and Coursera.

by ANJun 2, 2020

It was a good course to get a background and basic clearing while developing IIoT projects...

by DBJul 6, 2020

Very well designed and delivered Course. Thank you for the opportunity. Special Thanks to Prof Dave Sluiter

by ABApr 1, 2019

quizzes can be tougher.

great course!

100% history oriented.

View all reviews

About the Developing Industrial Internet of Things Specialization

The courses in this specialization can also be taken for academic credit as ECEA 5385-5387, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here.

In this specialization, you will engage the vast array of technologies that can be used to build an industrial internet of things deployment. You'll encounter market sizes and opportunities, operating systems, networking concepts, many security topics, how to plan, staff and execute a project plan, sensors, file systems and how storage devices work, machine learning and big data analytics, an introduction to SystemC, techniques for debugging deeply embedded systems, promoting technical ideas within a company and learning from failures. In addition, students will learn several key business concepts important for engineers to understand, like CapEx (capital expenditure) for buying a piece of lab equipment and OpEx (operational expense) for rent, utilities and employee salaries.

Developing Industrial Internet of Things

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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Project Planning and Machine Learning

About this Course

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