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📔 Sample notebook working with sensitive private data 👉 [ Ссылка ]
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Hi everyone, in today's video we'll be talking about working with private and sensitive data. A lot of times data scientists do not work on the most valuable and "cool" datasets, simply because these datasets contain too sensitive individual information. I have encountered this many times at my work, and it's frustrating! In this video, I will give you a brief introduction to different techniques for handling senstive data, and how to implement them using Antigranular platform.
Thank you for watching! 😇
🔑 TIMESTAMPS
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0:00 - Intro
1:00 - Antigranular/ Oblivious (sponsor)
1:22 - Overview of privacy-enhancing technologies
4:28 - Antigranular & competition 🏆
5:58 - What is Differential Privacy?
6:51 - Sample notebook walkthrough
7:49 - What is Secure Enclave?
8:34 - Sample notebook walkthrough (cont.)
11:07 - Training a regression model on private data
11:46 - Outro
👩🏻💻 COURSES & RESOURCES
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📖 Google Advanced Data Analytics Certificate 👉 [ Ссылка ]
📖 Google Data Analytics Certificate 👉 [ Ссылка ]
📖 Learn SQL Basics for Data Science Specialization 👉 [ Ссылка ]
📖 Excel Skills for Business 👉 [ Ссылка ]
📖 Machine Learning Specialization 👉 [ Ссылка ]
📖 Data Visualization with Tableau Specialization 👉[ Ссылка ]
📖 Deep Learning Specialization 👉 [ Ссылка ]
📖 Mathematics for Machine Learning and Data Science Specialization 👉 [ Ссылка ]
📖 Applied Data Science with Python 👉 [ Ссылка ]
🙋🏻♀️ LET'S CONNECT!
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Edited with Gling: [ Ссылка ]
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