Machine Learning in Chemical Processes via Python, Short Course | Part time online | University Teknologi PETRONAS | Malaysia
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Short Online

Machine Learning in Chemical Processes via Python

2 days
Duration
830 MYR/full
830 MYR/full
Unknown
Tuition fee
Anytime
Unknown
Apply date
Unknown
Start date

About

The Machine Learning in Chemical Processes via Python course offered by University Teknologi PETRONAS is designed to provide an exclusive insight for beginners on how machine learning can be effectively applied for descriptive (fault detection), diagnostic (fault classification) and predictive (prediction) analytics in chemical process systems.

Overview

What you will study

The practicality of the Machine Learning in Chemical Processes via Python course offered by University Teknologi PETRONAS is emphasized through the use of a distillation column example, which is one of the most commonly found unit operations in industrial processing plants.

Upon completion of this course, participants will be able to:

  • To provide beginners an easy to follow guide on familiarizing themselves with machine learning tools.
  • To apply Machine Learning tools in industrial chemical processes for descriptive (what happened), diagnostic (why it happened) and predictive (what will happen) analytics.

Programme Structure

The program focuses on:
  • 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 

Key information

Duration

  • Part-time
    • 2 days

Start dates & application deadlines

Language

English

Delivered

Online

Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Other requirements

General requirements

  • Undergraduate, Post-graduate students and researchers
  • Plant Operation Engineers
  • Process Engineer

Tuition Fee

To always see correct tuition fees
  • International

    830 MYR/full
    Tuition Fee
    Based on the tuition of 830 MYR for the full programme during 2 days.
  • National

    830 MYR/full
    Tuition Fee
    Based on the tuition of 830 MYR for the full programme during 2 days.

Funding

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Machine Learning in Chemical Processes via Python
University Teknologi PETRONAS
Machine Learning in Chemical Processes via Python
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University Teknologi PETRONAS

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