Introduction To Large Language Models (Llms) In Python

Develop Your Own Document-Reading Virtual Assistant With LLMs

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

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!

Overview

Section 1: Introduction To The Course

Lecture 1 Welcome To the Course

Lecture 2 Data and Code

Lecture 3 Python Installation

Lecture 4 Start With Google Colaboratory Environment

Lecture 5 Google Colabs and GPU

Lecture 6 Installing Packages In Google Colab

Lecture 7 Another Cloud To Work In: Saturn Cloud

Lecture 8 Say Hello To The Saturn Interface

Lecture 9 Brain Fail: Dealing With Memory Problems

Section 2: Get Started With The LLMs and Their Infrastructure

Lecture 10 What Is a Document Reading Virtual Assistant?

Lecture 11 Get Access To the OpenAI API

Lecture 12 Introduction to LangChain

Section 3: Start Reading in and Exploring Data

Lecture 13 Read in a Single PDF

Lecture 14 Read In Multiple PDFs

Lecture 15 A More Straightforward Way To Read in Multiple PDFs

Lecture 16 Learn More About Your Documents: Why We Need A Preliminary NLP Analysis

Lecture 17 Entity Matching

Lecture 18 Keyword Extraction

Lecture 19 What Is TF-IDF?

Lecture 20 Text Similarity

Section 4: Use LLMs To Learn From Your Text

Lecture 21 Overview-The Summarisation Process

Lecture 22 Abstract Summarizer

Lecture 23 Answer Questions Based On Given Text-LangChain

Lecture 24 Theoretical Undepinnings

Lecture 25 Answer Questions With Llama-Index

Section 5: Preliminary Prompt Engineering

Lecture 26 What Is Prompt Engineering?

Lecture 27 Prompt Engineering With Langchain

Section 6: Basic Python Primer

Lecture 28 Introduction to Numpy

Lecture 29 What Is Pandas?

Lecture 30 Basic Data Cleaning With Pandas

Lecture 31 Basic Principles of Data Visualisation

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)