Part of AAPG’s GeoAnalytics Credentialing Program, this two-day online AAPG Short Course, Excel-Based Random Forest Machine Learning Algorithms: Programming and Application 2.0, is set to take place 28–29 April 2020 for web-based participation only.
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Limited to only 35 spots, the first edition of the course sold out in just days. Learn how to build and use machine learning with the powerful and flexible Random Forest algorithm using Microsoft Excel.
This short course will be using WebEx and will run 9:00 am to 5:00 pm (CDT) each day providing hands-on interactive activities along with one-on-one coaching from the instructor, Alec Walker, Principal Consultant at Inly, after the course. This online course will be divided into two modules:
Module 1: Practical Software Development Using Visual Basic for Data Science in Excel
- This module provides an in-depth exploration of coding and data science techniques available in the Visual Basic (VBA) scripting language in Microsoft Excel.
Module 2: Random Forest Machine Learning Implementation in Visual Basic for Excel
- This module walks attendees through using and implementing the techniques from Module 1 in VBA and Excel to create a random forest tool, a powerful and flexible machine learning algorithm typically unavailable in Excel.
- Build and use a powerful tool capable of making valuable predictions from data
- Continue to adapt the tool to add new functionality or customization
- Develop and deploy basic software in Microsoft Excel using Visual Basic (VBA)
- Recognize opportunities for new data science software to add value
- Collect and organize data for use in data science projects in Microsoft Excel
- Identify free online tools to continue their learning outside of the course
- Determine when machine learning should be used on a problem
- Describe the random forest method and component steps in rigorous detail
- Explain random forest’s place in the world of machine learning
- Create effective pseudocode and commenting in larger-scale software
- Judge the effort and time requirements of software and data science projects
Why This Course Stands Out
You Keep the Tool –
The Random Forest tool that is developed in the class and its master copy from the instructor are production-ready and will be given to all attendees.
The Course is Built for You –
There are many courses teaching software engineering and data science. Rather than dilute the lessons in order to teach to a general audience, this course has been built for an oil and gas attendee skillset and use cases. All examples and exercises will reflect common workplace problems faced by attendees.
Move Quicker in Excel –
Learning to deploy Random Forest can take several weeks of training in graduate data science programs, but this course makes use of your existing knowledge of Excel to accelerate the learning. You can also skip all the parts about learning to create a robust back end and just rely on Excel spreadsheets for data storage.
Random Forests Add Considerable Value –
Random forests are widely applicable, do not require extremely large data sets, can be used for both regression and categorization, and are typically capable of yielding high accuracy results without extensive tuning. They are generally not available in Excel, but this workshop makes them so.
Understand Every Step –
Rather than teach advanced data science methodologies using off-the-shelf tools that do not allow visualization of intermediate steps, this course deliberately pushes all intermediate output into Excel, making it easy to see, understand, trust, and adapt everything created in the course for deployment on the job.
Here’s What Others are Saying
"I found the workshop to be very helpful, and the instructor to be very responsive."
– Bob Doty, Tall City Exploration
“The recent Excel-Based Random Forest ML course was a great introduction and starting point on both topics. I try to make it to one conference and one training course per year, but with an increase in AAPG online courses I could certainly increase that number.”
– Allan Hemmy, BNK Petroleum (US) Inc.
Hear from the instructor and get a sneak peak into the course.