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Project Planning and Machine Learning

Project Planning and Machine Learning

This course is part of Developing Industrial Internet of Things Specialization

Instructor: David Sluiter

13,477 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

132 reviews

Intermediate level

Recommended experience

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

132 reviews

Intermediate level

Recommended experience

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

What you'll learn

Create a plan to staff and execute a project to a schedule.
Create and calibrate sensor systems. Describe how hard drives and solid state drives operate. Describe how file systems for big data work.
Describe the basic operation of today's machine learning algorithms.
Describe the basics of big data analytics.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

91% of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

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Build your subject-matter expertise

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

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

Products don't design and build themselves. In this course, students learn how to staff, plan and execute a project to build a product. We explore sensors, which produce tremendous volumes of data, and then storage devices and file systems for storing big data. Finally, we study machine learning and big data analytics.

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Electrical Engineering (MS-EE) degree offered on the Coursera platform. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Electrical Engineering: https://www.coursera.org/degrees/msee-boulder

Module details

In this module I share with you my experience in product planning, staffing and execution. You will learn about the importance of first defining requirements, second performing the design work first, and then lastly writing the code.

What's included

8 videos4 readings1 assignment1 discussion prompt

8 videosTotal 128 minutes

1 - Course Overview3 minutes
1 - Introduction to Project Planning16 minutes
2 - Requirements and Scope18 minutes
3 - Roles and Responsibilities, Design Process Part 119 minutes
4 - Design Process Part 2 and Part 320 minutes
5 - Validation and the Agile Process24 minutes
6 - Documentation18 minutes
7 - Product Tear-down and Bill of Materials10 minutes

4 readingsTotal 18 minutes

Course Updates and Accessibility Support1 minute
Non-Credit Students: Welcome and Where to Find Help10 minutes
A Note from the Instructor5 minutes
Additional Course Resources2 minutes

1 assignmentTotal 10 minutes

Project Planning and Staffing Quiz10 minutes

1 discussion promptTotal 10 minutes

Introduction Discussion Forum10 minutes

In this module you will learn about sensors, and specifically a temperature sensor. You will learn how to calibrate a temperature sensor system, which helps you measure and improve the accuracy of that system. You will learn how data is stored on hard drives and solid state drives. You will learn about 3 file systems that are used to store large data sets, which are often referred to as "Big Data".

What's included

7 videos1 assignment

In this module we look at machine learning (ML), what it is and how it works. You will learn about several supervised learning algorithms and 1 unsupervised learning algorithm. No coding is required of you. Instead I provide working source code to you so you can play around with these algorithms. I wrap up by providing some examples of how ML can be used in the Industrial IoT space.

What's included

10 videos10 readings1 assignment

10 videosTotal 128 minutes

1 - Machine Learning Part 114 minutes
2 - Machine Learning Part 213 minutes
3 - Machine Learning Part 326 minutes
4 - Machine Learning Part 410 minutes
5 - Machine Learning Part 59 minutes
6 - Machine Learning Part 68 minutes
7 - Machine Learning Part 79 minutes
8 - Machine Learning Part 814 minutes
9 - Machine Learning Part 910 minutes
10 - Machine Learning Part 1015 minutes

10 readingsTotal 83 minutes

READ ME1 minute
Machine Learning Code Examples .zip file2 minutes
Required Reading: MLM - Test and Validation Datasets10 minutes
Optional Reading: The Human Promise of the AI Revolution10 minutes
Optional Reading: Lenovo - Debunking Misconceptions About AI10 minutes
Optional Reading: Wired - Neuroscience Key to True AI10 minutes
Optional Reading: IEEE Spectrum - 7 Revealings Ways AIs Fail10 minutes
Optional Reading: IEEE Spectrum - AI Risks10 minutes
Optional Reading: IEEE Spectrum - Don't Trust Deep Learning10 minutes
Optional Reading: IEEE Spectrum - Why Worry About Superintelligent AI10 minutes

1 assignmentTotal 24 minutes

Machine Learning Quiz24 minutes

In this module you will learn about big data and why we want to study it. You will learn about issues that can arise with a data set and the importance of properly preparing data prior to a Machine Learning exercise.

What's included

9 videos4 readings1 assignment

9 videosTotal 104 minutes

1 - Introduction Part 112 minutes
2 - Introduction Part 217 minutes
1 - Getting Started5 minutes
1 - Issues with Data Part 113 minutes
2 - Issues with Data Part 28 minutes
1 - Learning from Big Data Part 113 minutes
2 - Learning from Big Data Part 216 minutes
1 - Personal Example from the Instructor14 minutes
1 - Summary5 minutes

4 readingsTotal 134 minutes

Code for the NFL Prediction Example2 minutes
Please Watch: The 7 Principles of the IoT (and two other 7's)16 minutes
Optional Reading: Matillion - Data Lakes74 minutes
Optional Reading: Matillion - Data Analytics Platform42 minutes

1 assignmentTotal 24 minutes

Big Data Analytics Quiz24 minutes

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Build toward a degree

This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹