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An Introduction to Entity Resolution in Python

Name: An Introduction to Entity Resolution in Python
Price: 59 USD
Author: Paul Kinsvater

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.

4.7

63 lessons

2 projects

Created by Paul Kinsvater

An Introduction to Entity Resolution in Python

What's included

63 lessons across 10 modules
7 quizzes with instant feedback
2 projects in preconfigured environments
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

The ability to deduplicate records using Python
Familiarity with an entity resolution framework and business cases
An understanding of semantic similarity and search
Experience with classification in the context of entity resolution
Hands-on experience in data-centric AI using weak supervision and confident learning

Learning Roadmap

63 Lessons1 Project7 Quizzes1 Assessment

Introduction to Entity Resolution and Applications

Learn how to use entity resolution techniques in Python to improve data quality and integration.

About the Course

Deduplication vs. Linkage

Location Resolution: Geocoding

Master Data Management

Analytics in a Fragmented Data Landscape

Direct Impact with Payment Resolution

Real-Time Entity Resolution

A Quickstart Guide Using the RecordLinkage Package

Get started with entity resolution in Python using text preprocessing, similarity scoring, and evaluation techniques.

Solver’s Kitchen—Installation and Service for Restaurants

Effective Text Preprocessing

Similarity Features

Match vs. No-Match

Evaluate the Match Quality

Geocoders for Location Resolution

Sell with Insights

Quiz on Entity Resolution Fundamentals

Preprocessing

7 Lessons

Work your way through preprocessing text and location data for enhanced entity resolution.

Indexing

6 Lessons

Apply your skills to enhance the efficiency of entity resolution using various indexing techniques.

Feature Engineering

5 Lessons

Deepen your knowledge of feature engineering for entity resolution, exploring various similarity methods.

Pairwise Matching

12 Lessons

See how it works to tackle class imbalances and enhance binary classification in entity resolution.

Clustering

6 Lessons

Piece together the parts of clustering techniques to improve classification accuracy in entity resolution.

Integration

8 Lessons

Step through integrating entity resolution in data stacks via various platforms and services.

Entity Resolution Fundamentals

Assessment

Matching Products Across Two Online Shops

Project

Conclusion

Look at essential insights and skills for effective entity resolution in various systems.

Wrap Up

10.

Appendix

3 Lessons

Examine batch geocoding, vector search with LanceDB, and essential resources for entity resolution.

Auto-Tagging System for Content Categorization

Project

Premium

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

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.

ABOUT THE AUTHOR

Paul Kinsvater

I graduated in 2016 with a PhD in Statistics. Since then, I have pursued a career in data science. My preferred niche is entity resolution specifically and data quality in general.

Learn more about Paul

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