LLM-empowered Python Programming for AI

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LLM-empowered Python Programming for AI

This course is part of Applied AI for Engineers and Scientists: Practitioners Specialization

Instructors: Bo Liu

Included with Learn more

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Essential Python programming skills for applied AI
Use Large Language Models (LLMs) for Python programming

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Shareable certificate

Add to your LinkedIn profile

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This course is part of the Applied AI for Engineers and Scientists: Practitioners Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
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There are 3 modules in this course

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.

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:

What's included

7 videos5 readings1 assignment1 ungraded lab

7 videosTotal 62 minutes

Welcome to LLM-empowered Python Programming for AI4 minutes
Setup Python Environment9 minutes
Tutorial 1 - Python installation and a demo of Jupyter Notebook10 minutes
Variables, Operators, Control Structure, Loop, Functions11 minutes
AI-assisted Python Programming11 minutes
Tutorial 2 - Cases for using LLM for Python programming 17 minutes
Module 1 Recap1 minute

5 readingsTotal 55 minutes

Specialization and Course Structure5 minutes
Course Sample Certificate5 minutes
Connect with UofG Online5 minutes
PPT Lecture Slides - Python Fundamentals10 minutes
Module 1 Practice Assignment30 minutes

1 assignmentTotal 30 minutes

Module 1 Quiz30 minutes

1 ungraded labTotal 60 minutes

Assignment 1.1: Build a Student Score Analyzer60 minutes

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:

What's included

10 videos3 readings1 assignment2 ungraded labs

10 videosTotal 93 minutes

Module 2 Introduction1 minute
Objects and Classes in Python10 minutes
Python Lists12 minutes
Python Built-in Classes, Modules, Packages9 minutes
Data Analysis with NumPy and Pandas21 minutes
Data Visualization with Matplotlib5 minutes
Tutorial 1 - Getting Started with NumPy in PyCharm15 minutes
Tutorial 2 - AI-Assisted Pandas Analysis13 minutes
Tutorial 3 - AI-Assisted Data Visualisation with Matplotlib6 minutes
Module 2 Recap1 minute

3 readingsTotal 50 minutes

PPT Lecture Slides - Python Objects and Data Structures10 minutes
PPT Lecture Slides - Python Libraries and Numerical Programming10 minutes
Module 2 Practice Assignment30 minutes

1 assignmentTotal 30 minutes

Module 2 Quiz30 minutes

2 ungraded labsTotal 120 minutes

Assignment 2.1: Daily Sales Data Analyzer60 minutes
Assignment 2.2: Daily Temperature Data Analysis60 minutes

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:

What's included

8 videos5 readings1 assignment2 ungraded labs

8 videosTotal 87 minutes

Module 3 Introduction1 minute
Getting Started with scikit-learn10 minutes
Tutorial 1 - Linear Regression Case Study28 minutes
Tutorial 2 - KNN Classification Case Study14 minutes
Tutorial 3 - KNN Visualisation and Evaluation9 minutes
Tutorial 4 - Building Advanced Python Functions14 minutes
Tutorial 5 - Visualising Python Functions10 minutes
Module 3 Recap1 minute

5 readingsTotal 70 minutes

PPT Lecture Slides - Intro to scikit-learn10 minutes
PPT Lecture Slides & Tutorial Resources - Classification & Regression with scikit-learn10 minutes
PPT Lecture Slides - Complex Function Writing10 minutes
Module 3 Practice Assignment30 minutes
Continue Learning with UofG Online10 minutes

1 assignmentTotal 30 minutes

Module 3 Quiz30 minutes

2 ungraded labsTotal 120 minutes

Assignment 3.1: Build a Fruit Classifier with KNN60 minutes
Assignment 3.2: Predict Salaries with Linear Regression60 minutes

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Instructors

Bo Liu

University of Glasgow

6 Courses4,242 learners

Offered by

University of Glasgow

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