Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
6 Hour(s) 44 Minute(s)
Language
English
Taught by
Kris Celmer
Rating
Course Overview
Building Generative AI applications with powerful Large Language Models
ChatGPT revolutionises businesses, how we work and greatly influences our lives. It is much more than a famous Web and mobile applications everyone is using now. Its creators recently released a publicly available API enabling creation of sophisticated AI Apps utilising the power of GPT models to most difficult Natural Language Processing tasks and beyond.
This course aims at Python developers to teach how to harness the power of latest and greatest Large Language Models in custom, innovative applications, how to interface existing data in various formats with ChatGPT available through the API.
You will learn the magic of LangChain - the Python Library delivering ever growing ecosystem of tools and integrations necessary to build the AI Apps. LangChain offers not only convenient wrappers around ChatGPT model APIs, but has plenty of ready-made classes and functions facilitating creation and use of Chat Memory, Vector DBs for semantic search of relevant documents, and blueprints of powerful Agents, capable of using Python functions in your environment to get access to local, proprietary data.
The course is very practical and consists of dozens of practical demonstrations of Python code solving various AI tasks. You will get detailed, precise and in-depth explanation of all presented concepts and algorithms.
All of the code used in the course is available for your download from GitHub repository. You can use it as a basis to further exploration and experimentation leading to quick and easy development of real-life AI Apps.
Course Content
- 8 section(s)
- 43 lecture(s)
- Section 1 Course Introduction
- Section 2 Introduction to LLMs
- Section 3 Getting started with OpenAI API and LangChain
- Section 4 Prompt Engineering for Developers
- Section 5 Chatbots
- Section 6 Documents and Web pages
- Section 7 Vector Databases
- Section 8 Action planning agents
What You’ll Learn
- Understand Large Language Models and how to use them in AI Apps, how to prepare inputs, call GPT API endpoints and process outputs.
- Core Prompting Techniques: Instructions, Zero-Shot, Few-Shot, Self Confidence, Chain-of-Thoughts, Output Formatting to JSON Strings, etc.
- Using ChatGPT API in Natural Language Processing tasks: Named Entity Extraction, Classification, Sentiment Analysis, Translation.
- Manage chat history with LangChain Conversation Memory classes, including Window Buffer, Token Buffer and Summary, enabling sophisticated, intelligent chatbots.
- Use Embeddings to create Vector Databases and Semantic Similarity Search to retrieve relevant documents to chat with PDF or HTML docs and Source Code.
- Summarise long texts with LangChain Map-Reduce and Refine Chains, including PDFs, web pages, YouTube video transcripts.
- Use ReAct and OpenAI Functions Agent Classes to develop reasoning Agents capable of dealing with complex problems, requiring sequence of steps to solve.
- Build Chat applications to perform ad hoc Data Analysis with Pandas dataframes or SQL databases.
- Develop intuitions about how all these work and can be applied to solve real tasks.
- Learn latest and greatest Python libraries to build LLM enabled applications, including the famous LangChain.
Skills covered in this course
Reviews
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PPawel Sosnowski
It is an excellent course. The content is well-structured, and the examples are practical and inspiring. I highly recommend it to anyone looking to start building their own LLM-based applications :)
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SSanjeev Kumar
Great tutorial !!!
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EEran Ben Artzy
amazing lecturer, he is way beyond what I have looked.
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TTina Ferreira
I think the material is quite challenging, but the instructor is very good at explaining it.
