Ai, Machine Learning & Python For Chemical Engineers
Published 7/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 19h 15m | Size: 9.36 GB
AI and machine learning for process engineers, built in Python on real distillation and reactor examples

What you'll learn
Apply machine learning techniques to solve chemical engineering problems such as process modelling, parameter estimation, and fault detection.
Build and deploy Python-based AI models for applications like predictive analytics, optimization, and control in chemical processes.
Understand key AI concepts (supervised learning, unsupervised learning, neural networks, etc.) and how they relate to chemical process data.
Integrate chemical engineering domain knowledge with data science workflows, including data preprocessing, visualization, model evaluation
Requirements
Basic understanding of chemical engineering concepts (mass balance, unit operations, thermodynamics, etc.)
Familiarity with Python programming fundamentals (variables, loops, functions). No need to be an expert!
No prior experience with AI or machine learning is required - everything will be explained from the ground up with chemical engineers in mind
Description
This course contains the use of artificial intelligence.
In this course you will learnthe fundamentals of AI and machine learning, taught through chemical engineering so they actually click. Every concept is introduced the way a process engineer learns best: through familiar systems like column pressures, reactor conversions, and product specs, and coded from the ground up in Python. You come away understanding machine learning itself, not just a set of recipes to copy.
Every method begins with the question that matters most.Why does this technique work, and when does it fall short? You learn the reasoning before the code, so the fundamentals carry over to any dataset, any plant, and any problem you meet later. Every model is something you can open up and understand, rather than a black box.
You'll work with data drawn from genuine unit operations, including distillation columns, reactors, pipelines, wastewater treatment, crude oil blending, and NIR spectroscopy, and use it to build predictive models an engineer can trust. Along the way you will
- Predict a distillation column's product purity from its operating conditions, and see how a model learns the process
- Estimate reactor conversion and build a soft sensor for a quantity that's hard or slow to measure directly
- Identify off-spec batches and equipment faults early, using classification
- Predict product quality straight from NIR spectra, turning raw signal into a usable number
- Blend crude streams to hit target properties, and let the model find the combination that works
- Take a finished model and put it to use, running live predictions on new process data as it arrives
You'll match the right approach to each task, fromregression and classificationto ensemble models likerandom forests and XGB oost, alongside unsupervised techniques likeclustering and PCA for finding structure in process data, all in Python.
The course is designed for chemical and process engineers, students, and anyone in manufacturing, R&D, or plant operations who works with process data. The notebooks are written in a clean, flat style that stays easy to read, so a little Python is enough to follow along.
After completing the course, you'll be equipped to take a real process dataset, choose the right method, build a model, check that it holds up, and put it to work, with a solid understanding of the fundamentals behind it. That's practical machine learning, built on the engineering instinct you already have.
Who this course is for
Chemical engineering students who want to future-proof their careers by learning in-demand AI and Python skills
Process and design engineers looking to apply machine learning to simulation, modeling, optimization, or plant data
Academic researchers and graduate students aiming to enhance their experimental or simulation work using data-driven approach
Data science or software enthusiasts with a background in chemical engineering who want to transition into AI-powered process industries



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