Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
Learn Python Coding
Kanalga Telegram’da o‘tish
Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills. Admin: @HusseinSheikho || @Hussein_Sheikho
Ko'proq ko'rsatish📈 Telegram kanali Learn Python Coding analitikasi
Learn Python Coding (@pythonre) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 39 190 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 497-o'rinni va Hindiston mintaqasida 10 504-o'rinni egallagan.
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невідомо sanasidan buyon loyiha tez o‘sib, 39 190 obunachiga ega bo‘ldi.
11 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 445 ga, so‘nggi 24 soatda esa 15 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 2.22% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.91% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 870 marta ko‘riladi; birinchi sutkada odatda 358 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 4 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent math, harvard, oxford, supervision, waybienad kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 12 Iyun, 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.
39 190
Obunachilar
+1524 soatlar
+857 kunlar
+44530 kunlar
Postlar arxiv
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8. Set up the user interface and trigger the main function.
• Provides an input field for the user's question
• Triggers the main function when the user clicks "Get Answer"
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7. Define the main function to run all LLMs and aggregate results.
• Runs all reference models asynchronously
• Displays individual responses in expandable sections
• Aggregates responses using the aggregator model
• Streams the aggregated response.
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6. Implement the LLM call function.
• Asynchronously calls the LLM with the user's prompt
• Returns the model name and its response
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5. Define the models and aggregator system prompt.
• Specifies the LLMs to be used for generating responses
• Defines the aggregator model and its system prompt
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4. Initialize Together AI clients.
• Sets up Together API key as an environment variable
• Initializes both synchronous and asynchronous Together clients
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3. Set up the Streamlit app and API key input.
• Creates a title for the app
• Adds a secure input field for the Together API key
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2. Import necessary libraries
• Streamlit for the web interface
• asyncio for asynchronous operations
• Together AI for LLM interactions
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1. Install the necessary Python Libraries
Run the following commands from your terminal to install the required libraries:
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🧠 Build your own ChatGPT
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Exercises Course: Introduction to Web Scraping With Python
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- Parsing website data using string methods and regular expressions
- Parsing website data using an HTML parser
- Interacting with forms and other website components
Enroll: https://realpython.com/courses/exercises-introduction-web-scraping/
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39 195
Repost from Machine Learning with Python
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39 195
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🐍📺 Interacting With REST APIs and Python [Video]
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