Machine Learning in Chemical Processes via Python

Programme Structure

• Machine Learning Overview

• Why ML?

• General Application

• ML vs classic code 

• Model overview

• Steps in ML (Importing data to deploying model)

• How to evaluate ML Model (R2, MSE, RMSE, Confusion Matrix, Accuracy Paradox for CLASSIFICATION)

• Machine Learning for Descriptive Analytics

• Principal Component Analysis

• Fault Detection and Identification

• Hands on exercise and problem-solving: Distillation Column Case Study

• Machine Learning for Diagnostic and Predictive Analytics

• Python Basic Syntax and Modules ((NumPy, Pandas, Matplotlib, Scikit-Learn, XGBoost))

• Model development

• Training and Testing

• Feature Selection (if necessary)

• Hyperparameter Tuning

• Hands on exercise and problem-solving: Distillation Column Case Study