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Introduction to Large Language Models (LLMs) In Python
Highest Rated
Rating: 4.5 out of 5(2,199 ratings)
7,655 students

Introduction to Large Language Models (LLMs) In Python

Develop Your Own Document-Reading Virtual Assistant With LLMs
Highest Rated
Created byMinerva Singh
Last updated 11/2025
English

What you'll learn

  • Learn to work with Jupyter notebooks in a brand new cloud ecosystem-Saturn Cloud
  • Read in multiple PDFs into Python
  • Implement common natural language processing (NLP) techniques including entity recognition and keyword extraction
  • Get acquainted with common Large Language Model (LLM) frameworks including LangChain
  • Implement LLM frameworks for abstract summarisation and answering questions

Course content

7 sections • 35 lectures • 2h 56m total length

  • Welcome To the Course1:43
  • Data and Code0:09
  • Python Installation5:44
  • Start With Google Colaboratory Environment7:13

    Explore Google Colab, a cloud-based platform to run Jupyter Notebooks in your browser and save them in a Colab Notebooks folder on Google Drive.

  • Google Colabs and GPU5:50

    Learn how Google Colab provides access to gpu and tpu for deep learning, compares gpu and cpu, and enables hardware accelerator to run models efficiently.

  • Installing Packages In Google Colab4:27
  • Another Cloud To Work In: Saturn Cloud6:32
  • Say Hello To The Saturn Interface3:32
  • Brain Fail: Dealing With Memory Problems2:32

Requirements

  • Prior experience of using Jupyter notebooks
  • Prior exposure to Natural Language Processing (NLP) concepts will be helpful but not compulsory
  • An interest in using Large Language Models (LLMs) for your own documents

Description

Unlock the potential of large language models (LLM) with my comprehensive course: "Introduction to Large Language Models (LLMs) In Python." With a focus on LLM frameworks such as OpenAI, LangChain, and LLMA-Index, this course empowers you to build your own Document-Reading Virtual Assistant. Whether you're new to LLM implementation or seeking to advance your AI skills, this course offers an invaluable opportunity to explore the cutting-edge field of AI.


Course Highlights:


- Cloud-Based Python Environment: Harness the power of Saturn Cloud, a cloud-based Python environment, to implement robust LLM implementations.


- Practical Text Analysis: Learn to implement essential Natural Language Processing (NLP) techniques, including entity recognition and keyword extraction, to deconstruct the text documents


- Leveraging LLM Frameworks: Discover standard techniques for LLM frameworks, including LangChain, OpenAI and LLAMA-Index, for abstract summarization and querying.


Why Enroll in This Course?


By enrolling in this course, you're embarking on a journey to become an expert in harnessing the potential of text data with Large Language Models (LLMs). Driven by the vision of our experienced instructor, who holds an MPhil from the University of Oxford and a data-intensive PhD from Cambridge University, you'll receive the guidance needed to navigate the complexities of LLM implementation.


Beyond the course content, you'll benefit from continuous support, ensuring you extract the maximum value from your investment. Join our community of learners, immerse yourself in LLM analysis, and advance your expertise in AI and data science.


Enroll Now to Unlock the Power of Text Data With LLMs!

Who this course is for:

  • Students with prior exposure to NLP analysis
  • Those interested in using LLM frameworks for learning more about your texts
  • Students and practitioners of Artificial Intelligence (AI)

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