Explore entity resolution in Python, including use cases, semantic preprocessing, graph clustering, and weak supervision. Boost business value with hands-on coding and strategic decisions.

Hands-on Entity Resolution with Python-v3.png

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A typical business stores data across multiple systems, including ERPs for operations, a CRM for marketing, files, notebooks, and BI apps for other purposes. Records of the same customer (entity) exist in multiple places, likely not in sync across nor unique within sources. This inconsistent situation generates an opportunity for us to drive business value by cross-referencing and deduplicating records with entity resolution.

This course covers business acumen and hands-on coding. It starts with several business cases and a quick introduction to entity resolution in Python. Then, it explores semantic-preserving preprocessing, similarity feature engineering, graph clustering, weak supervision, confident learning, and integration.

As a developer, you’ll increase your company’s business value by developing and deploying entity resolution pipelines. As a decision-maker, you’ll know which solution best suits your business cases and how to negotiate the best value for your money.

An Introduction to Entity Resolution in Python

import pandas as pd

df = pd.DataFrame([
  [0, 'Universal IR/RF Aeros Remote Control- MX850/ Laser Etched Buttons/ Centrally Located Joystick/ Memory Back-Up/ One Hand Ergonomics/ Controls Up To 20 Components'],
  [1, 'Aeros IR/RF Remote Control - MX850 - TV, DVD Player, Audio Receivers, Cable Box, Satellite Receiver, PVR (Personal Video Recorder) - 100 ft - Universal Remote'],
  [3, 'Sony DVD-R Recordable Camcorder Media 3 Pack - 3DMR30L1H/ 30 Minute, 1.4 GB/ Accucore Technology/ Store Digital Video, Audio And Multimedia Files/ 3 Pack'],
  [4, 'Sony VAIO Neoprene Laptop Carrying Case - VGPAMC3/ Compatible With VAIO A Series 15 And FS Series 15.4 Widescreen Notebooks/ Helps Protect Your Notebook From Scratches']
], columns=['id', 'description'])

Learn how to embed texts in a vector space to detect similarities with LanceDB and KNN.

Introduction to Entity Resolution and Applications

A Quickstart Guide Using the RecordLinkage Package

Preprocessing

Indexing

Feature Engineering

Pairwise Matching

Clustering

Integration

Entity Resolution Fundamentals

Matching Products Across Two Online Shops

Conclusion

Appendix

Auto-Tagging System for Content Categorization

Embeddings and Vector Search with LanceDB