Predictive models that can accurately determine the output of a system, can help provide valuable insight & knowledge. Machine learning techniques can be used to create a predictive model when no knowledge of the system is known or difficult to determine.
Learn how to get started using machine learning tools to detect patterns and build predictive models from your datasets. In this webinar you will learn about several machine learning techniques available in MATLAB and how to quickly explore your data, evaluate machine learning algorithms, compare the results, and apply the best machine learning for your problem.
In this webinar, we will discuss:
- An overview of machine learning
- Machine learning models & techniques available in MATLAB (Decision trees, Neural Networks)
- Application Deployment into Excel
Case Studies in this demonstration:
- Fault Detection during Steel Plate Manufacturing
- Impurities Prediction of an Iron Ore Plant
About the Presenter:
David Willingham is a senior application engineer who specialises in data analytics for mining, finance and energy applications. He has a B.E. with honors in electrical and computer systems engineering from the Monash University.
