Data Privacy and Anonymization in Python
|
Learn to process sensitive information with privacy-preserving techniques.
Data privacy has never been more important. But how do you balance privacy with the need to gather and share valuable business insights? In this course, you’ll learn how to do just that, using the same methods as Google and Amazon—including data generalization and privacy models, like k-Anonymity and differential privacy. In addition to touching on topics such as GDPR, you’ll also discover how to build and train machine learning models in Python while protecting users’ sensitive information such as employee and income data. Let’s get started!
User Reviews
Be the first to review “Data Privacy and Anonymization in Python”
Related Products
Understanding GDPR
Gain a clear understanding of GDPR principles and how to set up GDPR-compliant processes in this comprehensive course.
Understanding Query Optimization in SQL Server
Retrieving the data from SQL Server is something that every data professional should know. However, retrieving the data from SQL Server in an efficient and performant way is a mastery of its own!
Culturing Resiliency with Data: A Taxonomy of Outages
This talk provides an overview of the categorization of outages that happened in Uber in the past few years based on root cause types.
Introduction to Data Versioning with DVC
Explore Data Version Control for ML data management. Master setup, automate pipelines, and evaluate models seamlessly.
Understanding Modern Data Architecture
Discover modern data architecture's key components, from ingestion and serving to governance and orchestration.
Deploying and Maintaining Assets in Power BI
Learn how to deploy and maintain assets in Power BI. You’ll get to grips with the Power BI Service interface and key elements in it like workspaces.
There are no reviews yet.