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Episode 18 — Data Collection and Preparation for AI
Manage episode 505486169 series 3689029
Jason Edwards에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Jason Edwards 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
Data is not just fuel for AI; it must be carefully gathered, cleaned, and prepared to produce reliable results. This episode breaks down the full lifecycle of data preparation, from collection through preprocessing. You’ll hear about structured, semi-structured, and unstructured data, and the importance of cleaning, labeling, and augmenting datasets. Normalization, handling missing values, and feature engineering are explained as key steps to ensure models learn from high-quality inputs.
We then cover broader issues like ethical collection, privacy, and regulatory compliance. Federated learning, human-in-the-loop labeling, and synthetic data generation are highlighted as innovative solutions to common bottlenecks. By the end, you’ll understand that successful AI projects live or die by their data pipelines, making preparation not a side task but the foundation of trustworthy intelligence. Produced by BareMetalCyber.com, where you’ll find more cyber prepcasts, books, and information to strengthen your certification path.
48 에피소드
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