Applied AI for Engineers and Scientists: Practitioners
University of Glasgow via Coursera Specialization
Overview
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This Specialization is designed for engineering and general science learners who want to use AI effectively, rather than build AI systems from scratch. While many AI courses are taught through a computer science lens, this program focuses on the needs of AI users: engineers and scientists applying AI to data analysis, optimization, programming, and practical problem-solving.
The content emphasizes core concepts, working principles, and applied workflows, making advanced AI techniques accessible without unnecessary mathematical or coding complexity. Through real-world case studies, you will learn how to select, implement, and evaluate AI methods in engineering and scientific contexts.
This is the second Specialization in the Applied AI for Engineers and Scientists series. It assumes learners have completed the Applied AI (Foundations) Specialization or have equivalent foundational knowledge.
In Applied AI: Practitioners, you will build deeper expertise in AI techniques for professional practice, including Python programming for applied AI, advanced evolutionary computation for intelligent optimization, and advanced machine learning for data analysis.
You will also learn how to use large language models (LLMs) to implement methods, validate results, and support method selection—reflecting how AI is increasingly used in engineering and scientific workflows.
By the end, you will be equipped to apply AI techniques confidently in real engineering and scientific environments.
Syllabus
- Course 1: LLM-empowered Python Programming for AI
- Course 2: Advanced Intelligent Optimization and its Applications
- Course 3: Advanced Machine Learning and its Applications
Courses
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The second course introduces advanced evolutionary computation methods for intelligent optimization and their applications. This course is built on the Applied AI Foundations Specialization, which introduced the fundamentals of genetic algorithms and particle swarm optimization. In this course, the configuration of intelligent optimization algorithms, handling constraints and multiple objectives, as well as combinatorial optimization, is introduced. Since almost all design problems in science and engineering can be formulated into optimization problems, after this course, you will become a professional designer armed with AI techniques, which is a big advantage compared to traditional experience-driven manual design techniques. This skill can be transferred to various domains in science and engineering.
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The third course introduces advanced machine learning techniques and their applications. This course is built on the Applied AI Foundations Specialization, which introduced the fundamentals of machine learning, and Course 1, which developed LLM-empowered Python programming skills for AI. In this course, advanced data preprocessing, machine learning outcome evaluation, neural network design and optimization, deep learning, and generative artificial intelligence are introduced. Since these techniques underpin essential modern AI systems in science and engineering, after completing this course, you will be able to develop, evaluate, and deploy advanced AI solutions using Python and LLM tools, providing a solid foundation for tackling real-world problems across a wide range of scientific and engineering domains.
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The first course introduces essential Python programming skills for applied AI. Thanks to Large Language Models (LLMs), you no longer need to be a proficient Python programmer to make good use of AI techniques in your science and engineering practice. Instead, you only need to handle the fundamentals of Python, its use for AI, and systematically describe your requirements for LLM tools. However, systematic LLM-empowered Python programming lessons are rare. This course aims to fill this gap. In 3 modules, you will be able to generate Python programs, understand them, and validate them, and become a proficient Python programmer, particularly in Python programs for numerical computing and AI. Students who already have strong Python programming experience can proceed directly to Course 2.
Taught by
Bo Liu and Xin Ma