Learn Quality 4.0 (Part 3)

When:  Jun 2, 2022 from 05:00 PM to 06:00 PM (UTC)
Associated with  Saskatchewan Section
AI and machine learning algorithms use inputted data to generate or predict outcomes. The input data contains many features which may not be in a format that can be directly given to a model. It would require some pre-processing. Feature Engineering is the process of preparing the input data in a form that can be utilized by the model or machine learning algorithm. It can be thought of as the art of selecting the important features and transforming them into refined and meaningful features that suit the needs of the model. To do this, various data engineering techniques such as selecting the relevant features, handling missing data, encoding the data, and normalizing it, are employed, thus greatly improving the performance of the machine learning model.

Event Time & Dtae: 11:00 am CST (SK)

June 02, 2022

Topics covered:

  • What is Feature Engineering?

  • Why is Feature Engineering important?

  • Analyzing Features in your Dataset

  • Handling Missing Data

  • Encoding Categorical Data

  • Feature Scaling

  • Feature Engineering tools and techniques

  • Application of Feature Engineering - Use case accident severity in London UK

  • Summary

Speaker: DENHOLM Kendall
DENHOLM is an Applied Science Engineering technologist, a Certified Lean Six Sigma Black Belt, an ASQ Certified Quality Auditor, Quality Engineer, and Certified Manager of Quality and Organizational Excellence. He is an ISO 9001 Quality Management Systems expert who builds Quality Management Systems from the ground up and leads companies to successful certification to ISO 9001. He is an expert in evaluating, measuring, and monitoring supplier performance and suppliers’ business processes and practices to reduce cost, mitigate risk and drive continuous improvement. He has co-authored the article “Part Manufacturer Assessment Process” in Quality and Reliability Engineering International magazine, published by John Wiley and Sons.
He is currently working in Quality Management, regularly analyzing, and summarizing large amounts of quality related data. After discovering some of the limitations of MS Excel, he started to learn how to use Microsoft Power BI business Intelligence application. His focus now is on learning to use this tool and other data analytics tools for analyzing big data to gain insights into quality issues.
In this session DENHOLM will talk about Feature Engineering. He will also share a use case of how Feature Engineering has been applied in a Machine Learning Model, to identify factors that influence accidents in London UK, to predict the likelihood of accident severity so that emergency resources can be efficiently deployed.