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Machine Learning

Kanalga Telegramโ€™da oโ€˜tish

Real Machine Learning โ€” simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Ko'proq ko'rsatish

๐Ÿ“ˆ Telegram kanali Machine Learning analitikasi

Machine Learning (@machinelearning9) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 40 072 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 398-o'rinni va Suriya mintaqasida 232-o'rinni egallagan.

๐Ÿ“Š Auditoriya koโ€˜rsatkichlari va dinamika

ะฝะตะฒั–ะดะพะผะพ sanasidan buyon loyiha tez oโ€˜sib, 40 072 obunachiga ega boโ€˜ldi.

23 Iyun, 2026 dagi oxirgi maโ€™lumotlarga koโ€˜ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 379 ga, soโ€˜nggi 24 soatda esa 30 ga oโ€˜zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya oโ€˜rtacha 1.92% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.16% ini tashkil etuvchi reaksiyalarni toโ€˜playdi.
  • Post qamrovi: Har bir post oโ€˜rtacha 770 marta koโ€˜riladi; birinchi sutkada odatda 466 ta koโ€˜rish yigโ€˜iladi.
  • Reaksiyalar va oโ€˜zaro taโ€™sir: Auditoriya faol: har bir postga oโ€˜rtacha 3 ta reaksiya keladi.
  • Tematik yoโ€˜nalishlar: Kontent distance, insidead, gpu, learning, degree kabi asosiy mavzularga jamlangan.

๐Ÿ“ Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโ€™riflaydi:
โ€œReal Machine Learning โ€” simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikhoโ€

Yuqori yangilanish chastotasi (oxirgi maโ€™lumot 24 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโ€˜lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim taโ€™sir nuqtasiga aylantirishini koโ€˜rsatadi.

40 072
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Postlar arxiv
๐Ÿ“Œ Building Robust Credit Scoring Models (Part 3) ๐Ÿ—‚ Category: MACHINE LEARNING ๐Ÿ•’ Date: 2026-03-20 | โฑ๏ธ Read time: 18 min re
๐Ÿ“Œ Building Robust Credit Scoring Models (Part 3) ๐Ÿ—‚ Category: MACHINE LEARNING ๐Ÿ•’ Date: 2026-03-20 | โฑ๏ธ Read time: 18 min read Handling outliers and missing values in borrower data using Python. #DataScience #AI #Python

๐Ÿ“Œ The Basics of Vibe Engineering ๐Ÿ—‚ Category: AGENTIC AI ๐Ÿ•’ Date: 2026-03-19 | โฑ๏ธ Read time: 14 min read Building products w
๐Ÿ“Œ The Basics of Vibe Engineering ๐Ÿ—‚ Category: AGENTIC AI ๐Ÿ•’ Date: 2026-03-19 | โฑ๏ธ Read time: 14 min read Building products without the coding part #DataScience #AI #Python

๐Ÿ“Œ Vibe Coding with AI: Best Practices for Human-AI Collaboration in Software Development ๐Ÿ—‚ Category: AGENTIC AI ๐Ÿ•’ Date: 20
๐Ÿ“Œ Vibe Coding with AI: Best Practices for Human-AI Collaboration in Software Development ๐Ÿ—‚ Category: AGENTIC AI ๐Ÿ•’ Date: 2026-03-19 | โฑ๏ธ Read time: 16 min read Accelerate coding with AI while staying in control and building reliable, production-ready software. #DataScience #AI #Python

๐Ÿ“Œ Linear Regression Is Actually a Projection Problem, Part 1: The Geometric Intuition ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 202
๐Ÿ“Œ Linear Regression Is Actually a Projection Problem, Part 1: The Geometric Intuition ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2026-03-19 | โฑ๏ธ Read time: 14 min read A visual guide to vectors and projections #DataScience #AI #Python

๐Ÿ“Œ Beyond Prompt Caching: 5 More Things You Should Cache in RAG Pipelines ๐Ÿ—‚ Category: AGENTIC AI ๐Ÿ•’ Date: 2026-03-19 | โฑ๏ธ Re
๐Ÿ“Œ Beyond Prompt Caching: 5 More Things You Should Cache in RAG Pipelines ๐Ÿ—‚ Category: AGENTIC AI ๐Ÿ•’ Date: 2026-03-19 | โฑ๏ธ Read time: 13 min read A practical guide to caching layers across the RAG pipeline, from query embeddings to fullโ€ฆ #DataScience #AI #Python

PhD Students - Do you need datasets for your research? Here are 30 datasets for research from NexData. Use discount code for
PhD Students - Do you need datasets for your research? Here are 30 datasets for research from NexData. Use discount code for 20% off: G5W924C3ZI 1. Korean Exam Question Dataset for AI Training https://lnkd.in/d_paSwt7 2. Multilingual Grammar Correction Dataset https://lnkd.in/dV43iqTp 3. High quality video caption dataset https://lnkd.in/dY9kxkhx 4. 3D models and scenes datasets for AI and simulation https://lnkd.in/dT-zscH4 5. Image editing datasets โ€“ object removal, addition & modification https://lnkd.in/dd8iCGMS 6. QA dataset โ€“ visual & text reasoning https://lnkd.in/dc3TNWFD 7. English instruction tuning dataset https://lnkd.in/dTeTgd2M 8. Large scale vision language dataset for AI training https://lnkd.in/dBJuxazN 9. News dataset https://lnkd.in/dYBJe5gd 10. Global building photos dataset https://lnkd.in/dVJsDXnC 11. Facial landmarks dataset https://lnkd.in/dz_KGCS4 12. 3D Human Pose & Landmarks dataset https://lnkd.in/dXE9ir8Z 13. 3D Hand Pose & Gesture Recognition dataset https://lnkd.in/d_QdGGb9 14. 14. Driver monitoring dataset โ€“ dangerous, fatigue https://lnkd.in/d6kF-9PW 15. Japanese handwriting OCR dataset https://lnkd.in/dHnriqrH 16. American English Male voice TTS dataset https://lnkd.in/dqyvg862 17. Riddles and brain teasers dataset https://lnkd.in/dKBHY3DE 18. Chinese test questions text https://lnkd.in/dQpUd8xC 19. Chinese medical question answering data https://lnkd.in/dsbWUCpz 20. Multi-round interpersonal dialogues text data https://lnkd.in/dQiUq_Jg 21. Human activity recognition dataset https://lnkd.in/dHM52MfV 22. Facial expression recognition dataset https://lnkd.in/dqQAfMau 23. Urban surveillance dataset https://lnkd.in/dc2RCnTk 24. Human body segmentation dataset https://lnkd.in/d6sSrDxS 25. Fashion segmentation โ€“ clothing & accessories https://lnkd.in/dptNUTz8 26. Fight video dataset โ€“ action recognition https://lnkd.in/dnY_m5hZ 27. Gesture recognition dataset https://lnkd.in/dFVPivYg 28. Facial skin defects dataset https://lnkd.in/dKCbUvU6 29. Smoke detection and behaviour recognition dataset https://lnkd.in/ddGg56R4 30. Weight loss transformation video dataset https://lnkd.in/dqqT4ed9 https://t.me/CodeProgrammer ๐Ÿ‘พ

๐Ÿ“Œ Why You Should Stop Worrying About AI Taking Data Science Jobs ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2026-03-18 | โฑ๏ธ Read tim
๐Ÿ“Œ Why You Should Stop Worrying About AI Taking Data Science Jobs ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2026-03-18 | โฑ๏ธ Read time: 8 min read Itโ€™s all just fearmongering #DataScience #AI #Python

๐Ÿ“Œ The New Experience of Coding with AI ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2026-03-18 | โฑ๏ธ Read time: 12 min read
๐Ÿ“Œ The New Experience of Coding with AI ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2026-03-18 | โฑ๏ธ Read time: 12 min read The seduction of AI code assistants #DataScience #AI #Python

Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

๐Ÿ“Œ Two-Stage Hurdle Models: Predicting Zero-Inflated Outcomes ๐Ÿ—‚ Category: MACHINE LEARNING ๐Ÿ•’ Date: 2026-03-18 | โฑ๏ธ Read tim
๐Ÿ“Œ Two-Stage Hurdle Models: Predicting Zero-Inflated Outcomes ๐Ÿ—‚ Category: MACHINE LEARNING ๐Ÿ•’ Date: 2026-03-18 | โฑ๏ธ Read time: 20 min read Why one model canโ€™t do two jobs #DataScience #AI #Python

Listen, hereโ€™s the crazy part: while everyoneโ€™s scared BTC might crash, insiders just dropped $350 MILLION buying the dip. Un
Listen, hereโ€™s the crazy part: while everyoneโ€™s scared BTC might crash, insiders just dropped $350 MILLION buying the dip. Unreal, right? The marketโ€™s playing a game where the biggest shorts might implode first-like a loaded gun cocked and ready. This isnโ€™t hype, itโ€™s cold, hard data from 14 years of trading wisdom. Wanna see how the pros move and actually win? Check this out ๐Ÿ‘‰ Scalping Kings No fluff, just profits. #ad InsideAds

CNN vs Vision Transformer โ€” The Battle for Computer Vision ๐Ÿ‘โšก๏ธ Two architectures. One goal: identify the cat. But they see t
CNN vs Vision Transformer โ€” The Battle for Computer Vision ๐Ÿ‘โšก๏ธ Two architectures. One goal: identify the cat. But they see things differently: ๐Ÿง  CNN (Convolutional Neural Network) ยท Scans the image with filters ยท Detects local patterns first (edges โ†’ textures โ†’ shapes) ยท Builds understanding layer by layer ๐Ÿ”„ Vision Transformer (ViT) ยท Splits image into patches (like words in a sentence) ยท Detects global patterns from the start ยท Sees the whole picture using attention mechanisms Same input. Same output. Different journey. CNNs think locally and build up. Transformers think globally from the get-go. Which one wins? Depends on the task โ€” but both are shaping the future of how machines see. https://t.me/CodeProgrammer

๐Ÿ“Œ Self-Hosting Your First LLM ๐Ÿ—‚ Category: LARGE LANGUAGE MODELS ๐Ÿ•’ Date: 2026-03-17 | โฑ๏ธ Read time: 20 min read Privacy. Co
๐Ÿ“Œ Self-Hosting Your First LLM ๐Ÿ—‚ Category: LARGE LANGUAGE MODELS ๐Ÿ•’ Date: 2026-03-17 | โฑ๏ธ Read time: 20 min read Privacy. Cost. Customization. Everything you need to knowโ€”step by step. #DataScience #AI #Python

๐Ÿ“Œ How to Effectively Review Claude Code Output ๐Ÿ—‚ Category: AGENTIC AI ๐Ÿ•’ Date: 2026-03-17 | โฑ๏ธ Read time: 7 min read Get mo
๐Ÿ“Œ How to Effectively Review Claude Code Output ๐Ÿ—‚ Category: AGENTIC AI ๐Ÿ•’ Date: 2026-03-17 | โฑ๏ธ Read time: 7 min read Get more out of your coding agents by making reviewing more efficient #DataScience #AI #Python

Time Complexity of 10 Most Popular ML Algorithms Know What You're Waiting For โณ๐Ÿง 
Time Complexity of 10 Most Popular ML Algorithms Know What You're Waiting For โณ๐Ÿง 

๐Ÿš€ ๐“๐Ž๐ ๐‘๐€๐† ๐ˆ๐๐“๐„๐‘๐•๐ˆ๐„๐– ๐๐”๐„๐’๐“๐ˆ๐Ž๐๐’ ๐€๐๐ƒ ๐€๐๐’๐–๐„๐‘๐’ โฃโฃ ๐Ÿ”น Advanced #RAG engineering conceptsโฃโฃ โ€ข Multi-stage retrieval pipelinesโฃโฃ โ€ข Agentic RAG vs classical RAGโฃโฃ โ€ข Latency optimizationโฃโฃ โ€ข Security risks in enterprise RAG systemsโฃโฃ โ€ข Monitoring and debugging production RAG systemsโฃโฃ โฃโฃ ๐Ÿ“„ ๐“๐ก๐ž ๐๐ƒ๐… ๐œ๐จ๐ง๐ญ๐š๐ข๐ง๐ฌ ๐Ÿ’๐ŸŽ ๐ฌ๐ญ๐ซ๐ฎ๐œ๐ญ๐ฎ๐ซ๐ž๐ ๐ช๐ฎ๐ž๐ฌ๐ญ๐ข๐จ๐ง๐ฌ ๐ฐ๐ข๐ญ๐ก ๐œ๐ฅ๐ž๐š๐ซ ๐ž๐ฑ๐ฉ๐ฅ๐š๐ง๐š๐ญ๐ข๐จ๐ง๐ฌ ๐ญ๐จ ๐ก๐ž๐ฅ๐ฉ ๐ฒ๐จ๐ฎ ๐ฎ๐ง๐๐ž๐ซ๐ฌ๐ญ๐š๐ง๐ ๐›๐จ๐ญ๐ก ๐œ๐จ๐ง๐œ๐ž๐ฉ๐ญ๐ฌ ๐š๐ง๐ ๐ฌ๐ฒ๐ฌ๐ญ๐ž๐ฆ ๐๐ž๐ฌ๐ข๐ ๐ง ๐ญ๐ก๐ข๐ง๐ค๐ข๐ง๐ .โฃโฃ โฃโฃ https://t.me/CodeProgrammer

๐Ÿ“Œ How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment ๐Ÿ—‚ Category: DEEP LEARNING ๐Ÿ•’ Date: 2026-
๐Ÿ“Œ How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment ๐Ÿ—‚ Category: DEEP LEARNING ๐Ÿ•’ Date: 2026-03-17 | โฑ๏ธ Read time: 18 min read Most neuro-symbolic systems inject rules written by humans. But what if a neural network couldโ€ฆ #DataScience #AI #Python

๐Ÿ“Œ Introducing Gemini Embeddings 2 Preview ๐Ÿ—‚ Category: LARGE LANGUAGE MODELS ๐Ÿ•’ Date: 2026-03-17 | โฑ๏ธ Read time: 10 min read
๐Ÿ“Œ Introducing Gemini Embeddings 2 Preview ๐Ÿ—‚ Category: LARGE LANGUAGE MODELS ๐Ÿ•’ Date: 2026-03-17 | โฑ๏ธ Read time: 10 min read One embedding model to rule them all #DataScience #AI #Python

๐Ÿ“Œ How to Build a Production-Ready Claude Code Skill ๐Ÿ—‚ Category: AGENTIC AI ๐Ÿ•’ Date: 2026-03-16 | โฑ๏ธ Read time: 11 min read
๐Ÿ“Œ How to Build a Production-Ready Claude Code Skill ๐Ÿ—‚ Category: AGENTIC AI ๐Ÿ•’ Date: 2026-03-16 | โฑ๏ธ Read time: 11 min read What I learned building and distributing my first Skill from scratch #DataScience #AI #Python

๐Ÿ“Œ Follow the AI Footpaths ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2026-03-16 | โฑ๏ธ Read time: 6 min read Shadow AI and
๐Ÿ“Œ Follow the AI Footpaths ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2026-03-16 | โฑ๏ธ Read time: 6 min read Shadow AI and the desire paths of modern work #DataScience #AI #Python