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Data Analytics

Data Analytics

Kanalga Telegram’da o‘tish

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

📈 Telegram kanali Data Analytics analitikasi

Data Analytics (@dataanalyticsx) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 29 838 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 4 345-o'rinni va Rossiya mintaqasida 21 557-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 29 838 obunachiga ega bo‘ldi.

26 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 302 ga, so‘nggi 24 soatda esa -2 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 4.91% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.53% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 1 465 marta ko‘riladi; birinchi sutkada odatda 456 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 2 ta reaksiya keladi.
  • Tematik yo‘nalishlar: Kontent sellerflash, buybox, buyer, chaos, effortless kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

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 27 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.

29 838
Obunachilar
-224 soatlar
+107 kunlar
+30230 kunlar
Postlar arxiv
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🏠🤖 Run Your Own LOCAL LLM (Beginner Friendly) LLMs are cool, but running your own local one hits different 😎 No cloud. No API keys. No limits. 🧩 Step 1: Install Ollama Install Ollama on your machine (works on Mac, Windows, Linux). Once installed, open your terminal. 🚀 Step 2: Run a model
ollama run llama3.2
This command: • Downloads the model • Starts it locally • Lets you chat instantly 💬 If you see the prompt, your local LLM is running. ⚙️ Step 3: Do local inference (API style) Ollama runs a local server on your machine.
curl http://127.0.0.1:11434/api/generate \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3.2",
    "prompt": "Explain overfitting like I am 12",
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  }'
If you get a JSON response with text → ✅ it works. 💡 Why this is powerful • Works offline • Private by default • Perfect for learning, testing, and small apps This is the easiest way to start with LLMs locally.

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