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Technical Course

Practical Application of Machine Learning for Pipeline Systems

A Hands-On Course, On-Line or On-Site

Course Content

A previous recording of all course content is available upon request as a playlist on our YouTube Channel. Please note course content is being continuously updated and actual agenda is dependent on attendee requirements and requested use cases. 

What You Will Learn
  • You will learn the basics of machine learning & how the process is used to support integrity and risk management

  • Data sampling, preparation & quality assurance methods

  • Feature analysis & engineering

  • Classification & cluster learning methods 

  • Regression learning methods 

  • Basics of inferential statistics & sampling

  • Outlier detection

  • Model validation

  • Where to go to learn more

Testimonials
  • "Instructor did a great job going over important concepts"

  • "Very good course, got me excited to utilize machine learning for my program"

  • "Great overview of machine learning"

  • "Very interesting and well delivered"

Software
  • Bring your own data, this is a hands-on course with instruction and use of software

  • Two versions are available for license after the course, however, attendees will also be provided resource links to build their own machine learning tools 

Schedule & Agenda
  • 2 Day on-line course duration is 9 - 4:00 US Mountain Time with 10 minute breaks at top of the hour, lunch & the last hour is for open & hands-on questions

  • All instructional material, software access and demonstration data is sent prior to the course after payment

  • We also hold courses on a per company basis to fit your schedule if there are at least six participants. In either case, please contact us by e-mail and we will provide further instructions.

Register
Prerequisites
  • This is a basic machine learning course presenting fundamental elements where you can quickly work thru the process with data familiar to you.

  • Prerequisites are familiarization with asset integrity & inspection concepts, proficiency in math & basic statistics, and ability to work with data (rows-columns).  

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