An Introduction to AI for Chemical Engineers from Udemy

An introductory course designed for helping engineering and chemistry STEM students and industry professionals entering the data science, AI, and machine learning areas.

This course is appropriate for those with minimal prior exposure to the field of AI and interested to either enter or shift their career path to this field and related areas. We use the simplest concepts in chemical engineering and chemistry, mainly the famous ideal gas law. to go over and introduce various topics related to AI and ML. In each step, we use simple, relevant, and area-specific examples to show how these concepts relate to real-world applications and systems in chemical engineering and chemistry fields.

Main topics covered in the course include:

Exact definition of AI and ML and the important terminology of the field
Main differences between different modeling approaches from purely data-driven models to mechanistic models
Definition of loss function and importance of selecting an appropriate one,
An introduction to artificial neural networks and deep learning
Overview of vision and language models
An introduction to cloud computing and its benefits.

The course concludes by going over several recommendations for taking the next steps necessary to continue your journey towards this dynamic, fast-growing, and exciting field.

What's inside

Learning objectives

Understand the definition of ai, machine learning, and other modeling approaches using simple chemeng examples
Understand the core ideas and principles behind ai/ml methods, including neural networks

Identify the right approach to a modeling problem
Get a high-level understanding of how large language and computer vision models work

Understand the definition of ai, machine learning, and other modeling approaches using simple chemeng examples
Understand the core ideas and principles behind ai/ml methods, including neural networks
Identify the right approach to a modeling problem
Get a high-level understanding of how large language and computer vision models work

Syllabus

Introduction

Understand the definition of AI and Machine Learning and their distinction. Principles of different modeling approaches from data-driven to hybrid modeling.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Uses the ideal gas law to introduce AI and ML, which provides a relevant and accessible entry point for chemical engineers and chemistry students

Provides an overview of vision and language models, which are essential for staying current with advancements in the field of artificial intelligence

Introduces cloud computing and its benefits, which is increasingly important for AI and ML applications in chemical engineering and chemistry

Covers the definition of loss functions and the importance of selecting an appropriate one, which is crucial for effective model building

Explores the differences between data-driven and mechanistic models, which is a fundamental concept for chemical engineers and chemistry professionals

Reviews summary

Ai intro for chemical engineers

According to learners, this course provides a clear and concise overview (positive) of AI and machine learning, specifically tailored with relevant chemical engineering examples (positive). Students with minimal prior exposure (positive) found it an excellent starting point (positive) for understanding core concepts and terminology. While it covers a broad range of topics (neutral) like neural networks, vision, and language models, many reviewers note it is strictly introductory (neutral) and lacks the depth and hands-on practice (warning) needed for practical application. It's recommended as a solid first step before further, more specialized study and is particularly valued by professionals seeking to understand how AI applies to their field and exploring career transitions (positive).

Covers NN, NLP, Vision, Cloud overview

"Covers a wide range of AI concepts from NNs to LLMs."

"Gives you a quick look at vision and language models."

"Provides a broad overview of various AI/ML topics and cloud computing."

Helpful advice for next steps

"The concluding recommendations for further learning were very useful."

"Helped me identify the next courses I need to take."

"Points you towards resources for continuing your AI journey."

"Good advice for professionals considering a shift into AI."

Provides a solid foundation for beginners

"Perfect if you have zero background in AI or data science..."

"Explained everything simply, great for getting started."

"This course is an excellent first step into the world of AI."

"I felt comfortable learning complex topics because of the simple explanations."

Uses simple, relevant ChemEng examples

"Using simple concepts like the ideal gas law made complex AI ideas clear."

"The chemical engineering context was incredibly helpful for understanding applications."

"I appreciated the area-specific examples used throughout."

"The connection to chemical engineering problems felt very relevant to my work."

Introductory only, lacks practical detail

"It's very high-level; doesn't go deep enough for implementation."

"I was hoping for more hands-on coding or projects, but it was mostly theory."

"You will need other resources for practical application after this."

"Covers many topics but doesn't delve deeply into any single one."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in An Introduction to AI for Chemical Engineers with these activities:

Review Ideal Gas Law

Show steps

Refresh your understanding of the Ideal Gas Law, as it's a foundational concept used throughout the course to illustrate AI/ML principles.

Browse courses on Ideal Gas Law

Show steps

Review the Ideal Gas Law equation and its components.
Work through practice problems involving the Ideal Gas Law.
Relate the Ideal Gas Law to chemical engineering principles.

Review: 'Python for Data Analysis' by Wes McKinney

Show steps

Familiarize yourself with Python data analysis tools to better understand the practical applications of AI/ML in chemical engineering.

View Python for Data Analysis on Amazon

Show steps

Read the introductory chapters on Pandas and NumPy.
Practice data manipulation techniques using the book's examples.
Apply these techniques to chemical engineering datasets.

Build a Predictive Model for Gas Properties

Show steps

Apply the AI/ML concepts learned in the course to predict gas properties based on the Ideal Gas Law and other relevant parameters.

Show steps

Gather a dataset of gas properties and related parameters.
Choose an appropriate AI/ML model for prediction.
Train and evaluate the model's performance.
Refine the model based on evaluation results.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Create a Blog Post on AI in Chemical Engineering

Show steps

Solidify your understanding of AI applications in chemical engineering by writing a blog post explaining key concepts and examples.

Show steps

Research current applications of AI in chemical engineering.
Outline the key topics to cover in the blog post.
Write the blog post, explaining concepts clearly and concisely.
Include relevant examples and visuals.
Publish the blog post on a relevant platform.

Follow a Tutorial on Neural Networks with TensorFlow

Show steps

Deepen your understanding of neural networks by following a hands-on tutorial using TensorFlow, a popular deep learning framework.

Show steps

Find a suitable TensorFlow tutorial for neural networks.
Set up the TensorFlow environment.
Work through the tutorial, understanding each step.
Experiment with different parameters and architectures.

Review: 'Deep Learning' by Goodfellow, Bengio, and Courville

Show steps

Gain a deeper theoretical understanding of deep learning models and their underlying principles.

View Deep Learning on Amazon

Show steps

Read the chapters on foundational deep learning concepts.
Study the different neural network architectures.
Understand the mathematical principles behind deep learning.

Presentation: AI Applications in Chemical Plants

Show steps

Create a presentation showcasing real-world applications of AI in chemical plants to demonstrate your understanding of the course material.

Show steps

Research AI applications in chemical plants.
Prepare a presentation outline.
Create visually appealing slides with clear explanations.
Practice the presentation delivery.

Career center

Learners who complete An Introduction to AI for Chemical Engineers will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer develops, implements, and maintains machine learning models and algorithms. This role involves designing experiments, building data pipelines, and deploying models into production systems. This course helps provide a foundation by covering the definition of AI and Machine Learning, using chemical engineering examples to illustrate core principles. The course's introduction to neural networks, training methods, and different modeling approaches is directly applicable to the tasks a Machine Learning Engineer undertakes. Taking this course helps one enter the field of Machine Learning Engineering.

See salaries and explore the career path for Machine Learning Engineer

AI Developer

An AI Developer specializes in creating and implementing AI-powered solutions. This often involves coding, testing, and deploying AI models. The course's introduction to neural networks and deep learning architectures, as well as its overview of vision and language models, helps build a foundation for an AI Developer. Furthermore, the discussion of cloud computing benefits is relevant. The AI Developer also needs to understand the difference between various modeling approaches, as covered in the course, which helps one to enter the field of AI.

See salaries and explore the career path for AI Developer

Machine Learning Scientist

Machine Learning Scientists explore new algorithms and techniques to enhance machine learning models. The role includes publishing research papers. The course's introduction to neural networks and deep learning helps build a base for Machine Learning Scientists. The information on various modeling approaches helps. Those wishing to enter the field as a Machine Learning Scientist, which typically requires a graduate degree, may find this course useful.

See salaries and explore the career path for Machine Learning Scientist

Data Scientist

Data Scientists analyze large datasets to extract meaningful insights and drive data-informed decision-making. A key component of this role is model building and evaluation. This course may be useful due to its coverage of AI and machine learning definitions, loss functions, and data-driven versus mechanistic models. The course introduces neural networks, language models, and vision models. This is useful for Data Scientists who need to apply these techniques. This course gives one an advantage entering the Data Science field.

See salaries and explore the career path for Data Scientist

Computational Chemist

Computational Chemists use computational techniques to solve chemical problems. This includes developing and applying models to simulate chemical processes and predict molecular properties. This course's introduction to AI and machine learning using chemical engineering examples is very relevant. The coverage of data-driven modeling, loss functions, and neural networks will provide the basis to explore more advanced computational chemistry techniques. Computational chemists typically require a graduate degree, and this course may be useful when entering this field.

See salaries and explore the career path for Computational Chemist

AI Consultant

An AI Consultant advises organizations on how to leverage AI to solve business problems and improve performance. Consultants need to possess a strong understanding of AI technologies and their applications across various industries. This course helps one build a stronger understanding of AI. The discussion of different modeling approaches, neural networks, and cloud computing helps to provide tools for working in the field as an AI Consultant.

See salaries and explore the career path for AI Consultant

Process Automation Engineer

Process Automation Engineers design, implement, and optimize automated systems for chemical processes. These engineers may leverage AI and machine learning techniques to enhance process efficiency and safety. This course helps build some of the AI and machine learning knowledge necessary for this role. Specifically, the course's focus on data-driven modeling and neural networks helps one build a foundation in process automation. The material on cloud computing is also relevant. This course may be useful in entering the field of process automation.

See salaries and explore the career path for Process Automation Engineer

AI Product Manager

AI Product Managers guide the development and launch of AI-powered products. This requires a strategic understanding of AI capabilities and market needs. This course helps one better understand the technical aspects of AI and machine learning. The course's overview of vision and language models, as well as the discussion of cloud computing, provides valuable context. Overall, this course is useful for one considering a career as an AI Product Manager.

See salaries and explore the career path for AI Product Manager

Research Scientist

Research Scientists conduct research to advance scientific knowledge. They need to be able to design and conduct experiments, analyze data, and publish findings. This course helps one build a better understanding of machine learning and AI. The course's discussion of different modeling approaches, loss functions, and neural networks helps equip a Research Scientist with valuable tools. The concepts presented in this course may be useful when entering the field as a Research Scientist, which typically requires a graduate degree.

See salaries and explore the career path for Research Scientist

Data Analyst

A Data Analyst collects, cleans, and analyzes data to provide insights and support decision-making. This role typically involves using statistical software and data visualization tools. This course may be useful because it helps introduce the definitions of AI and machine learning. The course's discussion of different modeling approaches and the distinctions between data-driven and mechanistic models are helpful. This course can help build a foundation for entering the field as a Data Analyst.

See salaries and explore the career path for Data Analyst

AI Research Engineer

AI Research Engineers design and implement AI projects. They need expertise in algorithms, data structures, and software engineering principles. The AI Research Engineer must possess knowledge of Neural Networks. This course's coverage of AI and ML definitions may provide some knowledge. The course may be useful when starting a career as an AI Research Engineer.

See salaries and explore the career path for AI Research Engineer

Robotics Engineer

Robotics Engineers design, develop, and test robots. This involves integrating mechanical, electrical, and computer systems to create functional robots. This course helps one with the AI and Machine Learning aspects of being a Robotics Engineer. Specifically, the course helps build knowledge of language models, loss functions, and computer vision. This may be useful for someone looking for a career as a Robotics Engineer.

See salaries and explore the career path for Robotics Engineer

Process Engineer

Process Engineers optimize industrial processes to improve efficiency, reduce costs, and ensure safety. Process engineers may use data analysis tools. This course may be useful due to its introduction of AI and machine learning. In particular, this course helps build the knowledge required to understand data driven process improvement. This can help you build a foundation in this field.

See salaries and explore the career path for Process Engineer

Manufacturing Engineer

Manufacturing Engineers design, develop, and maintain manufacturing processes including optimizing workflows and ensuring product quality. This course helps provide a foundation by covering AI fundamentals. The course's introduction to machine learning, neural networks and deep learning directly helps the Manufacturing Engineer. This may be useful for one's career.

See salaries and explore the career path for Manufacturing Engineer

Quantitative Analyst

Quantitative Analysts develop and implement mathematical and statistical models for financial analysis and risk management. This role demands a strong background in mathematics, statistics, and programming. Though focused on chemical engineering examples, this course's introduction to AI and machine learning, specifically regarding different modeling approaches and neural networks, helps equip one with tools that a Quantitative Analyst might use. This course can help build a foundation when entering the field of Quantitative Analysis.

See salaries and explore the career path for Quantitative Analyst

An Introduction to AI for Chemical Engineers

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Ai intro for chemical engineers

Activities

Career center

Reading list

Share

Similar courses