Data Analytics
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Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho
Ko'proq ko'rsatish
Tarmoq:📈 Telegram kanali Data Analytics analitikasi
Data Analytics (@dataanalyticsx) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 29 843 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 4 349-o'rinni va Rossiya mintaqasida 21 576-o'rinni egallagan.
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невідомо sanasidan buyon loyiha tez o‘sib, 29 843 obunachiga ega bo‘ldi.
27 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 277 ga, so‘nggi 24 soatda esa 1 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
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Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 28 Avgust, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
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Obunachilar
+124 soatlar
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+27730 kunlar
Postlar arxiv
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🚀 𝐑𝐀𝐆 𝐢𝐬 𝐨𝐧𝐥𝐲 𝐚𝐬 𝐠𝐨𝐨𝐝 𝐚𝐬 𝐲𝐨𝐮𝐫 𝐂𝐇𝐔𝐍𝐊𝐈𝐍𝐆 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲
𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐑𝐀𝐆) 𝐢𝐬 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐡𝐨𝐰 𝐰𝐞 𝐛𝐮𝐢𝐥𝐝 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬—𝐛𝐮𝐭 𝐡𝐞𝐫𝐞’𝐬 𝐭𝐡𝐞 𝐬𝐞𝐜𝐫𝐞𝐭 𝐦𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐦𝐢𝐬𝐬:
👉 𝐓𝐡𝐞 𝐰𝐚𝐲 𝐲𝐨𝐮 𝐬𝐩𝐥𝐢𝐭 𝐲𝐨𝐮𝐫 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐬 (𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠) 𝐝𝐢𝐫𝐞𝐜𝐭𝐥𝐲 𝐝𝐞𝐭𝐞𝐫𝐦𝐢𝐧𝐞𝐬 𝐡𝐨𝐰 𝐚𝐜𝐜𝐮𝐫𝐚𝐭𝐞, 𝐟𝐚𝐬𝐭, 𝐚𝐧𝐝 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐲𝐨𝐮𝐫 𝐑𝐀𝐆 𝐬𝐲𝐬𝐭𝐞𝐦 𝐰𝐢𝐥𝐥 𝐛𝐞.
💡 𝐁𝐚𝐝 𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠 = 𝐢𝐫𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐚𝐧𝐬𝐰𝐞𝐫𝐬, 𝐰𝐚𝐬𝐭𝐞𝐝 𝐭𝐨𝐤𝐞𝐧𝐬, 𝐚𝐧𝐝 𝐡𝐢𝐠𝐡𝐞𝐫 𝐜𝐨𝐬𝐭𝐬.
💡 𝐒𝐦𝐚𝐫𝐭 𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠 = 𝐩𝐫𝐞𝐜𝐢𝐬𝐞 𝐫𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥, 𝐜𝐨𝐧𝐭𝐞𝐱𝐭-𝐫𝐢𝐜𝐡 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞𝐬, 𝐚𝐧𝐝 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲.
𝐀𝐟𝐭𝐞𝐫 𝐝𝐞𝐞𝐩 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡, 𝐈 𝐩𝐮𝐭 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫 𝐚 𝐠𝐮𝐢𝐝𝐞 𝐨𝐧 𝐭𝐡𝐞 𝐓𝐎𝐏 𝟏𝟓 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐞𝐯𝐞𝐫𝐲 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐬𝐡𝐨𝐮𝐥𝐝 𝐤𝐧𝐨𝐰:
🔹 𝘍𝘪𝘹𝘦𝘥-𝘚𝘪𝘻𝘦 𝘊𝘩𝘶𝘯𝘬𝘪𝘯𝘨 – 𝘴𝘪𝘮𝘱𝘭𝘦, 𝘱𝘳𝘦𝘥𝘪𝘤𝘵𝘢𝘣𝘭𝘦
🔹 𝘙𝘦𝘤𝘶𝘳𝘴𝘪𝘷𝘦 𝘊𝘩𝘢𝘳𝘢𝘤𝘵𝘦𝘳 𝘚𝘱𝘭𝘪𝘵𝘵𝘪𝘯𝘨 – 𝘧𝘢𝘴𝘵 & 𝘴𝘤𝘢𝘭𝘢𝘣𝘭𝘦
🔹 𝘚𝘦𝘮𝘢𝘯𝘵𝘪𝘤 𝘊𝘩𝘶𝘯𝘬𝘪𝘯𝘨 – 𝘮𝘦𝘢𝘯𝘪𝘯𝘨-𝘣𝘢𝘴𝘦𝘥 𝘱𝘳𝘦𝘤𝘪𝘴𝘪𝘰𝘯
🔹 𝘋𝘰𝘤𝘶𝘮𝘦𝘯𝘵-𝘚𝘱𝘦𝘤𝘪𝘧𝘪𝘤 – 𝘭𝘦𝘷𝘦𝘳𝘢𝘨𝘦 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦 (𝘗𝘋𝘍𝘴, 𝘏𝘛𝘔𝘓, 𝘔𝘢𝘳𝘬𝘥𝘰𝘸𝘯)
🔹 𝘏𝘪𝘦𝘳𝘢𝘳𝘤𝘩𝘪𝘤𝘢𝘭 – 𝘱𝘢𝘳𝘦𝘯𝘵-𝘤𝘩𝘪𝘭𝘥 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯𝘴𝘩𝘪𝘱𝘴
🔹 𝘚𝘦𝘯𝘵𝘦𝘯𝘤𝘦-𝘈𝘸𝘢𝘳𝘦 – 𝘳𝘦𝘢𝘥𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘱𝘳𝘦𝘴𝘦𝘳𝘷𝘦𝘥
🔹 𝘛𝘰𝘬𝘦𝘯-𝘉𝘢𝘴𝘦𝘥 – 𝘢𝘭𝘪𝘨𝘯𝘦𝘥 𝘸𝘪𝘵𝘩 𝘓𝘓𝘔 𝘵𝘰𝘬𝘦𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯
🔹 𝘚𝘭𝘪𝘥𝘪𝘯𝘨 𝘞𝘪𝘯𝘥𝘰𝘸 – 𝘰𝘷𝘦𝘳𝘭𝘢𝘱𝘱𝘪𝘯𝘨 𝘤𝘰𝘯𝘵𝘦𝘹𝘵
🔹 𝘛𝘰𝘱𝘪𝘤-𝘉𝘢𝘴𝘦𝘥 – 𝘵𝘩𝘦𝘮𝘢𝘵𝘪𝘤 𝘤𝘭𝘶𝘴𝘵𝘦𝘳𝘪𝘯𝘨
🔹 𝘗𝘳𝘰𝘱𝘰𝘴𝘪𝘵𝘪𝘰𝘯-𝘉𝘢𝘴𝘦𝘥 – 𝘭𝘰𝘨𝘪𝘤𝘢𝘭 𝘶𝘯𝘪𝘵 𝘴𝘱𝘭𝘪𝘵𝘴
🔹 𝘊𝘰𝘯𝘵𝘦𝘹𝘵-𝘈𝘸𝘢𝘳𝘦 – 𝘕𝘓𝘗-𝘥𝘳𝘪𝘷𝘦𝘯 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘴
🔹 𝘈𝘨𝘦𝘯𝘵𝘪𝘤 – 𝘓𝘓𝘔𝘴 𝘤𝘩𝘶𝘯𝘬 𝘭𝘪𝘬𝘦 𝘩𝘶𝘮𝘢𝘯𝘴
🔹 𝘚𝘮𝘢𝘭𝘭-𝘵𝘰-𝘉𝘪𝘨 – 𝘱𝘳𝘦𝘤𝘪𝘴𝘪𝘰𝘯 + 𝘤𝘰𝘯𝘵𝘦𝘹𝘵
🔹 𝘚𝘵𝘢𝘵𝘪𝘴𝘵𝘪𝘤𝘢𝘭 – 𝘥𝘢𝘵𝘢-𝘥𝘳𝘪𝘷𝘦𝘯 𝘣𝘰𝘶𝘯𝘥𝘢𝘳𝘪𝘦𝘴
🔹 𝘔𝘰𝘥𝘢𝘭𝘪𝘵𝘺-𝘚𝘱𝘦𝘤𝘪𝘧𝘪𝘤 – 𝘵𝘦𝘹𝘵, 𝘵𝘢𝘣𝘭𝘦𝘴, 𝘪𝘮𝘢𝘨𝘦𝘴, 𝘤𝘰𝘥𝘦
✨ 𝐏𝐫𝐨 𝐭𝐢𝐩: 𝐓𝐡𝐞𝐫𝐞’𝐬 𝐧𝐨 𝐨𝐧𝐞-𝐬𝐢𝐳𝐞-𝐟𝐢𝐭𝐬-𝐚𝐥𝐥. 𝐓𝐡𝐞 𝐛𝐞𝐬𝐭 𝐑𝐀𝐆 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐮𝐬𝐞 𝐡𝐲𝐛𝐫𝐢𝐝 𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐭𝐚𝐢𝐥𝐨𝐫𝐞𝐝 𝐭𝐨 𝐭𝐡𝐞𝐢𝐫 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐧𝐝 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞.
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Repost from Machine Learning with Python
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LLMs are the new operating system for work. 🚀💻
But most people still don’t know the difference between RAG, Embeddings, and Hallucinations. 🤔🧠
Here’s the vocabulary cheat sheet everyone in AI should know 📚✨
These foundational LLM concepts every professional, creator, founder, and tech enthusiast should know 👩💼👨💻🎨🚀
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https://t.me/DataAnalyticsX 📎
