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Learn why token counts can’t prove AI agent ROI, and why companies should measure accepted outcomes, human effort, and the value left after all costs.
#DataScience #AI #ArtificialIntelligence #ODSC #AIAgents #AgenticAI #AIROI #EnterpriseAI
hubs.li/Q04C7Zss0
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thank you for applying to #akademibootcamp2026! 🇭🇹

we appreciate everyone across haiti who took this first step.

the selection process is still underway.

check your email and follow our channels for updates.

learn today. lead tomorrow. ✳️

#akademi #datascience
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Average & Peak Concurrent Viewers of English News Channels across Primary & All Streams on 24 October 2026, from 1000 Hrs to 2200 Hrs. #Ratingology #RatingsKaBaap #DataScience
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Average & Peak Concurrent Viewers of Hindi News Channels across Primary & All Streams on 24 October 2026, from 1000 Hrs to 2200 Hrs. #Ratingology #RatingsKaBaap #DataScience
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Ever noticed how Netflix knows exactly what you'll binge next? Recommendation engines analyze patterns in your viewing history to predict your next choice. Fascinating, right? 🧠🎬 #DataScience #MindReading
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🌍 Data Scientist Expert
$115/hr · Remote (Global)

Author realistic data science problems from your own work and build each one into a reference answer used to evaluate frontier AI models. Flexible hours, contractor.

#DataScience #RemoteJobs #Hiring
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Key point: standardize first. Z=(5.8−5.6)/1=0.2, then read CDF: P(Z<0.2)=0.5793. Don’t “eyeball” normal curves—convert to Z and use CDF.
#DataScience #Statistics #InterviewPrep
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Replying to @WOkparaji74619
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My whole approach in one line: trust the evidence, not the first result.
How do you work through a new ML problem? 👇
#MachineLearning #DataScience #BuildInPublic
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🏠 Predict House Sale Prices with Spark Machine Learning
Hands-on project to master Big Data & ML skills in real estate analytics 🚀

👉 Start building: buff.ly/Pev77dg

#ApacheSpark #BigData #DataScience #MachineLearning #AI #Analytics #PredictiveModeling #DataEngineer
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Replying to @RosanaFerrero
🔹 GEE: Para datos longitudinales centrados en el promedio poblacional.
🔹 Zero-Inflated / Hurdles: Para exceso de ceros.
🔹 GLMM & GAMM: Combinan familias no normales, relaciones no lineales y efectos aleatorios jerárquicos en una sola estructura. ⚡

#DataScience #Estadistica
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Data Scientist and Machine Learning Engineers, please lets connect on this whatsapp group
#DataScience #MachineLearning #Python
I just started a WhatsApp group for data scientists and ML engineers.
Share projects. Ask questions. Debug together. Get honest feedback.
All levels welcome. Join: chat.whatsapp.com/E4MLxuE8ooNInT…

#DataScience #MachineLearning #BuildInPublic
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Árboles de decisión = el modelo que juega a las 20 preguntas. Así elige cada corte con impureza de Gini y por qué si crece sin límite termina memorizando (sobreajuste) + cómo frenarlo. #MachineLearning #DataScience #IA
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