LLM Fundamentals in Python

Name: LLM Fundamentals in Python
Availability: RequiresRegistration
Rating: 4.8 (359 reviews)

skill Path

This path gives you the core skills to work with large language models in Python. You’ll start by understanding how AI chatbots work, then learn to use LLM APIs programmatically — managing conversations, applying prompt engineering, and implementing advanced patterns like function calling and MCP. You’ll finish by building interactive web applications that integrate with AI models using Streamlit.

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4.8 (359 reviews)

9,923 learners enrolled in this path.

Intermediate friendly
1 month (5 hrs/week)
Self paced
4 Courses
1 project

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Otávio Silveira

Data Analyst @ Hortifruti

LLM Fundamentals in Python

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Path overview

Learn to work with large language models through APIs, prompt engineering, and advanced patterns like function calling and MCP. Then put your skills to work building interactive AI-powered web applications with Streamlit.

Key skills

Understanding the capabilities and limitations of AI chatbots and large language models
Using the OpenAI Chat Completions API to build AI-driven applications
Applying prompt engineering techniques and implementing advanced patterns including function calling and MCP
Building interactive AI-powered web applications using Streamlit

Path outline

LLM Fundamentals in Python [4 courses]

Course 1: AI Chatbots: Harnessing the Power of Large Language Models with Chandra 3h

Course Objectives

Understand the basics of AI, machine learning, deep learning, natural language processing, and chatbots.
Learn how to craft effective prompts and interact with chatbots to improve learning outcomes.
Explore practical use cases for AI chatbots in education, work, and personal projects.
Gain hands-on experience using Chandra on the Dataquest platform.

Course 2: Prompting Large Language Models in Python 6h

Course Objectives

Utilize OpenAI's Chat Completions API to generate tailored AI-driven responses
Manage conversation histories to maintain context in AI conversations
Create custom Python functions for dynamic interactions with large language models
Learn prompt engineering techniques to guide AI responses effectively
Regulate token usage within the OpenAI API framework for efficient scripting
Adopt best practices in prompt engineering to improve the quality of AI-generated text

Course 3: Tool Use with LLMs in Python 6h

Course Objectives

Generate validated, structured outputs from LLM responses
Implement agentic loops that handle multi-step tool execution
Create reusable tool servers using the Model Context Protocol
Design prompt templates and pipelines for reliability
Handle errors and validation failures in LLM workflows

Course 4: Designing Dynamic Python Applications with Streamlit 4h

Course Objectives

Grasp the essentials of Streamlit for interactive web app development.
Create user-friendly interfaces with Streamlit's array of widgets.
Manage application state for dynamic user experiences.
Integrate AI LLM models for responsive chatbots.
Leverage Streamlit's latest chat widgets for effective communication.

Projects in this path

Developing a Dynamic AI Chatbot

For this project, you’ll become a developer at a tech company, using Python and the OpenAI API to create an engaging AI chatbot. You’ll gain skills in conversation management, persona creation, and token handling as you build a chatbot that adapts to different platforms.

View Project

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