Veri doğrulama yaklaşımları

Bir Yapay Zekâ Eğitim projesi, aday öğrencilere öğrenme stilleri, performansları ve tercihleri temelinde çalışma materyalleri önerecek bir model oluşturmayı hedefliyor. Önerilerin adil olmasını sağlamak için verileri doğrulaman gerekiyor.

Aşağıdaki adımlardan hangileri veri doğrulamayla ilişkilidir? Uygulanan üç yanıtı seç.

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Sorumlu AI Veri Yönetimi

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Kursa Ücretsiz Başlayın

Learn about the fundamental theory behind responsible data management in AI. You’ll review key dimensions such as security, transparency, fairness, and more before conceptualizing the metrics and challenges associated with these dimensions and understanding how to balance responsible AI with other business and technical requirements.

Exercise 1: Responsible data dimensions Exercise 2: Developing AI responsibly Exercise 3: Dimensions of responsible data Exercise 4: Responsible AI metrics Exercise 5: Planning a responsible AI project Exercise 6: Responsible data use Exercise 7: Fairness in AI projects Exercise 8: Challenges of responsible AI Exercise 9: Trade-offs in responsible AI Exercise 10: Professional duties and ethical conduct

Data regulation is essential to the legality of any AI project. Learn about key regulations, third-party licenses, and compliance strategies for informed consent and data-sharing agreements (with legal counsel). Finally, you'll learn about developing robust data governance strategies and management plans to ensure your project remains compliant throughout its lifecycle.

Exercise 1: Overview of data regulation Exercise 2: Data protection laws Exercise 3: Data regulation in a project Exercise 4: Data compliance Exercise 5: Data owner rights and compliance Exercise 6: Data use agreements (DUAs)Exercise 7: Third-party licensing Exercise 8: Types of licenses Exercise 9: Licensing agreements Exercise 10: Selecting a license Exercise 11: Data governance and data management plan (DMP)Exercise 12: Data management Exercise 13: Components of a DMP

Navigate through the responsible selection and integration of data sources by understanding the importance of data origin, nature, and temporality, emphasizing legal compliance, diversity, and fairness. By exploring types of bias and their origins, you’ll look at data fairness and representation to create a comprehensive dataset for modeling.

Exercise 1: Data sources Exercise 2: Data source types Exercise 3: Data source and responsible dimensions Exercise 4: Data source limitations Exercise 5: Limitations in data sources Exercise 6: Data sources and bias Exercise 7: Data source selection Exercise 8: Evaluation of data sources Exercise 9: Data augmentation Exercise 10: Data integration Exercise 11: Data integration steps Exercise 12: Risks and benefits of data integration

Understand data audits, data validation, and bias mitigation. Data pre-processing and catching bias in modeling do not sound like fun, but let's streamline them with common approaches and trusted techniques!

Exercise 1: Veri denetimi Exercise 2: Proje yaşam döngüsünde veri denetimi Exercise 3: Veri denetimi gereksinimi Exercise 4: Veri doğrulama Exercise 5: Veri doğrulamayı tanımlama Exercise 6: Veri doğrulama yaklaşımları

Geçerli Egzersiz

Exercise 7: Veri doğrulamada en iyi uygulamalar Exercise 8: Alt grup analizi Exercise 9: Projede veri doğrulama Exercise 10: Ön işleme ve önyargı Exercise 11: Önyargı azaltma Exercise 12: Azaltma stratejileri Exercise 13: Proje yaşam döngüsü boyunca önyargı azaltma Exercise 14: Önyargı azaltmanın sonuçları Exercise 15: Yayın sonrası önyargı Exercise 16: Tebrikler!