Engineering Solutions & Software
Technical Course
Practical Application of Machine Learning for Pipeline Systems
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A Hands-On Course, On-Line or On-Site
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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
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You will learn the basics of machine learning & how the process is used to support integrity and risk management
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Data sampling, preparation & quality assurance methods
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Feature analysis & engineering
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Classification & cluster learning methods
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Regression learning methods
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Basics of inferential statistics & sampling
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Outlier detection
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Model validation
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Where to go to learn more
Testimonials
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"Instructor did a great job going over important concepts"
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"Very good course, got me excited to utilize machine learning for my program"
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"Great overview of machine learning"
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"Very interesting and well delivered"
Software
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Bring your own data, this is a hands-on course with instruction and use of software
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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
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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
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All instructional material, software access and demonstration data is sent prior to the course after payment
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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
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To register, please contact michaelgloven@pipeline-risk.com and indicate your preferred course date
Prerequisites
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This is a basic machine learning course presenting fundamental elements where you can quickly work thru the process with data familiar to you.
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Prerequisites are familiarization with asset integrity & inspection concepts, proficiency in math & basic statistics, and ability to work with data (rows-columns).