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The Course (Videos)
Practical Application of Machine Learning - Asset Integrity & Risk
Virtual & On-Site

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

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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"

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Interactive Software

  • Software is provided for on-site courses

  • The secure web based application supports the use of your data to experience how machine learning brings value to asset  integrity management

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Schedule & Agenda

  • 2 Day on-line course duration is 9 - 1:00 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.

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Register

  • To register, please contact michaelgloven@pipeline-risk.com and indicate your preferred course date

  • We also hold courses on a per company basis to fit your schedule if there are at least six participants

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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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