Veri Bilimi

Veri bilimi, istatistiksel analiz, makine öğrenimi ve tahmine dayalı modelleme yoluyla kuruluşların verilerden eyleme geçirilebilir içgörüler elde etmelerini sağlar. Veriye dayalı karar verme ve dijital dönüşüm çabalarını desteklemek için araçları, teknikleri, gerçek dünya uygulamalarını ve en iyi uygulamaları inceliyoruz.

Veri Bilimi Keşfedin

MLOps

ML ve AI Modelleri İçin 57 Veri Seti

Veri BilimiHaz 10

Data is required to leverage or build generative AI or conversational AI solutions. You can use existing datasets available on the market or hire a data collection service. We identified 57 datasets to train and evaluate machine learning and AI models.

Devamını Oku

Veri BilimiMay 11

Yapay Zeka Veri Kalitesi: Zorluklar & En İyi Uygulamalar

Poor data quality delays the successful deployment of AI and ML projects. Even the most advanced AI algorithms can yield flawed results if the underlying data is of low quality.

Veri BilimiNis 24

Graph Veritabanı Benchmark: Neo4j vs FalkorDB vs Memgraph

We benchmarked Neo4j, FalkorDB, and Memgraph on a synthetic graph derived from 120,000 Amazon product reviews (381K nodes, 804K edges).

Veri BilimiŞub 20

Yedekli Öğrenme: 7 Kullanım Alanı & Örnekler

According to recent McKinsey analyses, the most pressing risks of AI adoption include model hallucinations, data provenance and authenticity, regulatory non-compliance, and AI supply chain vulnerabilities. Federated learning (FL) has emerged as a foundational technique for organizations seeking to mitigate these risks.

Veri BilimiOca 27

En İyi Kod Olmayan ML Platformları: ChatGPT Alternatifleri

We benchmarked 4 no-code machine learning platforms across key metrics: data processing (handling missing values, outliers), model setup and ease of use, accuracy metrics output, availability of visualizations, and any major limitations or notes observed during testing. No-code machine learning tools benchmark Note: Scores represent average performance across kNN and Logistic Regression where applicable.