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Project Planning and Machine Learning

University of Colorado Boulder via Coursera

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  • Provider Coursera
  • Subject Project Management
  • $ Cost Free Online Course (Audit)
  • Session Upcoming
  • Language English
  • Certificate Paid Certificate Available
  • Start Date
  • Duration 4 weeks long

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Overview

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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

Syllabus

WEEK 1

Project Planning and Staffing

In this module I share with you my experience in product planning, staffing and execution. You will perform a product tear down and build a bill of materials (BOM) for that product.

 

WEEK 2

Sensors and File Systems

In this module you will learn about sensors, and in this case, a temperature sensor. You will learn how to calibrate and then validate that a temperature sensor is producing accurate results. We will study how data is stored on hard drives and solid state drives. We will take a brief look at file systems used to store large data sets.

 

WEEK 3

Machine Learning

In this module we look at machine learning, what it is and how it works. We take a look at a couple 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 IIoT space.

 

WEEK 4

Big Data Analytics

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 ML exercise.

 

Taught by

David Sluiter

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