Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

LLM-empowered Python Programming for AI

University of Glasgow via Coursera

Go to class Write review

Details

Go to class

Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

3 weeks, 5 hours a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Part of

Applied AI for Engineers and Scientists: Practitioners

Overview

Google, IBM & Meta Certificates – 40% Off

One Coursera Plus subscription covers most Professional Certificates on Coursera.

Unlock All Certificates

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.

Syllabus

Python Fundamentals and LLM-based Programming
- This module introduces the fundamentals of Python. Even with assistance from Large Language Models (LLMs), the concepts introduced in this module must be addressed for learners to understand and verify the LLM’s outputs. Then, LLM-based Python programming is introduced with examples. After learning this module, you will be able to:
Python Data Structures, Libraries, and Numerical Programming
- In this module, you will build a stronger foundation in Python by learning how objects and classes work, and by using core data structures such as lists, tuples, sets, and dictionaries. You will be introduced to powerful libraries/packages, including NumPy, Pandas, and Matplotlib, for numerical computing, data analysis, and visualization, which are foundational components in the use of AI. Finally, you will learn how LLM tools can support advanced numerical programming and help you solve more complex computational tasks. After learning this module, you will be able to:
LLM-assisted Python programming for AI
- In this module, you will learn the fundamentals of scikit-learn, one of the most widely used machine learning libraries in Python. You will follow a typical machine learning workflow, including preparing data, training models, and evaluating results. The module will also show how LLM tools can help you control scikit-learn and generate machine learning programs. By the end, you will develop advanced Python programming techniques using LLM tools. After learning this module, you will be able to: