Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Solving Real-World Data Science Problems with LLMs - Historical Document Analysis

Keith Galli via YouTube

Start learning Write review
Boot.dev Ad

The Most Addictive Python and SQL Courses

Learn More → Corporate Finance Institute Ad

Build the Finance Skills That Lead to Promotions, Not Just Certificates

Learn More →

Overview

Google, IBM & Meta Certificates – 40% Off
One Coursera Plus subscription covers most Professional Certificates on Coursera.
Unlock All Certificates
A Python project tutorial that uses hosted and open-source LLMs to reconnect pages from historical documents, extract names, ages, and locations, and analyze the resulting records.

Syllabus

- Video Overview & Reference Material
- Data & Code Setup
- Task #0: Configure LLM to use with Python OpenAI API
- Task #0 continued: LLM Configuration with Open-Source Model LLama 2 via Ollama
- Task #1: Use LLM to Parse Simple Sentence Examples
- Sub-task #1: Convert string to Python Object
- Task #1 continued: Use Open-Source LLM to Parse Sentence Examples w/ LangChain
- Quick note on a benefit of using LangChain easily switching between models
- Task #2 warmup: Grab Apprenticeship Agreement rows from Dataframe
- Task #2: Connect Pages that Belong to the Same Documents
- Task #3: Parse out values from merged documents
- Task #4 setup: Analyze Results
- Fixing up our results from task #3 quickly
- Task #4: Find the average age of apprentices in our merged contract documents
- Other analysis, wlho had the most apprentices?

Taught by

Keith Galli

Reviews

Start your review of Solving Real-World Data Science Problems with LLMs - Historical Document Analysis

Start learning

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.