Numerical Methods & Machine Learning for Chemical Engineering
About This Course
This course equips learners with skills in numerical methods and machine learning, tailored for engineering and analytics professionals seeking industry upskilling. Learners gain practical skills in numerical algorithms, regression modelling, integration, and differential equation solutions, addressing real-world optimisation and prediction challenges. Through hands-on computational work, learners develop competencies in data analysis, model evaluation, and data-driven decision-making. The course supports career advancement by enhancing employability in sectors such as engineering, analytics, and technology, meeting industry needs for professionals skilled in numerical methods and machine learning. Ideal for those aiming to upgrade their roles in today’s data-centric landscape.
As this micro-credential course is offered in the FlexiMasters in Chemical Engineering and FlexiMasters in Computational Chemical Modelling and AI, learners are required to enrol into the programme and complete all required courses within the respective programmes.
What You'll Learn
• Develop and use numerical algorithms to solve optimization and curve fitting problems.
• Develop and use numerical algorithms to solve integration and differential equations.
• Develop linear regression models and evaluate its goodness of fit criteria and common pitfalls.
• Apply machine learning to regression and classification problems.
Entry Requirements
No pre-requisites are needed to enroll in the individual courses; however, learners without qualifications or work experience in relevant engineering or the related fields may find the course contents challenging.
Note: Shortlisting will be conducted for the courses