Download A Gentle Introduction to AI for Chemical Engineers 2025
A Gentle Introduction to AI for Chemical Engineers udemy video course download, what is AI and ML and what are basic principles behind building AI and ML models.? An introductory course designed for helping to engineer 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.
What you’ll learn
- 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
Course content
- Section 1: Introduction
- Lecture 1 Introduction
- Section 2: AI and Definitions
- Lecture 2 What is AI? What is Machine Learning?
- Lecture 3 Predictor and Response Variables
- Lecture 4 More discussion of features
- Lecture 5 Data-driven, Mechanistic, and Hybrid Modeling
- Lecture 6 Different data types
- Section 3: Building a Model: From Regression to Importance of the Loss Function
- Lecture 7 Linear regression
- Lecture 8 How to train a model and what is loss function
- Lecture 9 Data-driven Vs. Mechanistic models
- Lecture 10 Classification Vs. Regression
- Section 4: Introduction to Deep Learning Models
- Lecture 11 Introduction to neural networks
- Lecture 12 On training neural networks
- Lecture 13 Gradience descent method
- Section 5: On Language and Vision Models
- Lecture 14 Boom of deep learning models
- Lecture 15 Text and image processing: Computer vision models
- Lecture 16 Text and image processing: Language models
- Lecture 17 GPT and other decoder Large Language Models (LLM)
- Lecture 18 Complexities of deep learning models
- Section 6: Brief Introduction to Cloud Computing
- Lecture 19 Cloud computing and its benefits
- Section 7: Final Remarks and Recommendations
- Lecture 20 Final remarks and recommendations for next
Who this course is for:
- STEM students, chemical and mechanical engineering, and chemistry major students interested in getting into data science, AI and machine learning areas.
- Early-career chemical and mechanical engineers interested in AI, machine learning, and data science areas.
Course details
- Video quality: MP4 | Video: h264, 1280 × 720
- Audio quality: Audio: AAC, 44.1 KHz, 2 Ch
- Published 01/2025
- Video duration: 1h 9m
- Number of lessons: 07 sections, 20 lectures
- Language: Language: English
- Compressed file size: 490 MB
