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코드 따라하기 ↗Using Synthetic Data for Machine Learning & AI in Python
Rewatch this training to discover what synthetic data is, how it protects privacy, and how it's being used to accelerate AI adoption in banking, healthcare, and many other industries.
Alexandra Ebert
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실습 문제하고 트랙하세요.
Google로 계속하기계속 진행하시면 다음 사항에 동의하는 것으로 간주됩니다: 이용약관 및 개인정보 처리방침 그리고 귀하의 데이터가 미국에 저장된다는 점에 동의하는 것으로 간주됩니다.관련

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What is Synthetic Data?
Synthetic data is artificially generated data that mimics the characteristics of real-world data without containing any actual information.

튜토리얼
Creating Synthetic Data with Python Faker Tutorial
Generating synthetic data using Python Faker to supplement real-world data for application testing and data privacy.
튜토리얼
Preprocessing in Data Science (Part 3): Scaling Synthesized Data
You can preprocess the heck out of your data but the proof is in the pudding: how well does your model then perform?



80% of AI projects fail, and more don't even start due to privacy constraints. This is where AI-generated synthetic data comes in. It's an anonymization technology seen as the key enabler for artificial intelligence.
Rewatch this training to discover what synthetic data is, how it protects privacy, and how it's being used to accelerate AI adoption in banking, healthcare, and many other industries. You will create a highly representative synthetic dataset yourself, learn how to assess its quality and use it for privacy-preserving machine learning. And as a bonus exercise, we'll look into smart imputation with synthetic data to save you time on data pre-processing!
Key Takeaways:
Additional Resources
Code along with Alexandra on DataLab
Generate synthetic data using MOSTLY AI - Use the ‘AI/ML training’ set