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Machine Learning

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

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Machine Learning (@machinelearning9) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 40 323 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 332-o'rinni va Suriya mintaqasida 225-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 40 323 obunachiga ega bo‘ldi.

09 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 378 ga, so‘nggi 24 soatda esa 30 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 2.23% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.95% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 897 marta ko‘riladi; birinchi sutkada odatda 788 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
  • Tematik yo‘nalishlar: Kontent distance, insidead, gpu, learning, degree kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Yuqori yangilanish chastotasi (oxirgi ma’lumot 10 Iyul, 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.

40 323
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Postlar arxiv
📌 A Clear Intro to MCP (Model Context Protocol) with Code Examples 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-25
📌 A Clear Intro to MCP (Model Context Protocol) with Code Examples 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-25 | ⏱️ Read time: 16 min read MCP is a way to democratize access to tools for AI Agents. In this article…

📌 Testing the Power of Multimodal AI Systems in Reading and Interpreting Photographs, Maps, Charts and More 🗂 Category: LAR
📌 Testing the Power of Multimodal AI Systems in Reading and Interpreting Photographs, Maps, Charts and More 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-03-25 | ⏱️ Read time: 30 min read Can multimodal AI systems consisting in LLMs with vision capabilities understand figures and extract information…

📌 Data-Driven March Madness Predictions 🗂 Category: DATA SCIENCE 🕒 Date: 2025-03-25 | ⏱️ Read time: 11 min read How to opt
📌 Data-Driven March Madness Predictions 🗂 Category: DATA SCIENCE 🕒 Date: 2025-03-25 | ⏱️ Read time: 11 min read How to optimize your bracket systematically, no college basketball knowledge required

📌 Attractors in Neural Network Circuits: Beauty and Chaos 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-03-25 | ⏱️ Read time:
📌 Attractors in Neural Network Circuits: Beauty and Chaos 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-03-25 | ⏱️ Read time: 12 min read Neural networks under a different lens: generating basins of attraction in a shift register NN

📌 The Ultimate AI/ML Roadmap For Beginners 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-25 | ⏱️ Read time: 10 min r
📌 The Ultimate AI/ML Roadmap For Beginners 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-25 | ⏱️ Read time: 10 min read How to learn AI/ML from scratch

📌 Uncertainty Quantification in Machine Learning with an Easy Python Interface 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-0
📌 Uncertainty Quantification in Machine Learning with an Easy Python Interface 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-03-26 | ⏱️ Read time: 15 min read The ML Uncertainty Package

📌 AI Agents from Scratch: Iterations & Chains 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-26 | ⏱️ Read time: 7 min
📌 AI Agents from Scratch: Iterations & Chains 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-26 | ⏱️ Read time: 7 min read From Zero to Hero using only Python & Ollama (no GPU, no APIKEY)

📌 Automate Supply Chain Analytics Workflows with AI Agents using n8n 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-2
📌 Automate Supply Chain Analytics Workflows with AI Agents using n8n 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-26 | ⏱️ Read time: 6 min read What if you could automate complete supply chain analytics workflows  with low-code solutions?

📌 About Towards Data Science 🗂 Category: ABOUT 🕒 Date: 2025-03-27 | ⏱️ Read time: 2 min read We strive to present well-wri
📌 About Towards Data Science 🗂 Category: ABOUT 🕒 Date: 2025-03-27 | ⏱️ Read time: 2 min read We strive to present well-written, informative articles that our audience is excited to read.

📌 How to Streamline Your Work with Agents and LLMs 🗂 Category: THE VARIABLE 🕒 Date: 2025-03-27 | ⏱️ Read time: 3 min read
📌 How to Streamline Your Work with Agents and LLMs 🗂 Category: THE VARIABLE 🕒 Date: 2025-03-27 | ⏱️ Read time: 3 min read This week, we focus on helping you improve your workflow with AI.

📌 Talk to Videos 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-03-27 | ⏱️ Read time: 28 min read Developing an interactiv
📌 Talk to Videos 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-03-27 | ⏱️ Read time: 28 min read Developing an interactive AI application for video-based learning in education and business

📌 Japanese-Chinese Translation with GenAI: What Works and What Doesn’t 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03
📌 Japanese-Chinese Translation with GenAI: What Works and What Doesn’t 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-27 | ⏱️ Read time: 20 min read Evaluating GenAI in Japanese-Chinese translation: current limits and opportunities

📌 Data Science: From School to Work, Part III 🗂 Category: DATA SCIENCE 🕒 Date: 2025-03-27 | ⏱️ Read time: 12 min read Good
📌 Data Science: From School to Work, Part III 🗂 Category: DATA SCIENCE 🕒 Date: 2025-03-27 | ⏱️ Read time: 12 min read Good practices for Python error handling and logging

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📌 From Physics to Probability: Hamiltonian Mechanics for Generative Modeling and MCMC 🗂 Category: MATH 🕒 Date: 2025-03-28
📌 From Physics to Probability: Hamiltonian Mechanics for Generative Modeling and MCMC 🗂 Category: MATH 🕒 Date: 2025-03-28 | ⏱️ Read time: 17 min read Hamiltonian mechanics is a way to describe how physical systems, like planets or pendulums, move…

📌 AI Agents from Scratch: Multi-Agent System 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-28 | ⏱️ Read time: 14 min
📌 AI Agents from Scratch: Multi-Agent System 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-28 | ⏱️ Read time: 14 min read From Zero to Hero using only Python & Ollama (no GPU, no APIKEY)

📌 Master the 3D Reconstruction Process: A Step-by-Step Guide 🗂 Category: DATA SCIENCE 🕒 Date: 2025-03-28 | ⏱️ Read time: 1
📌 Master the 3D Reconstruction Process: A Step-by-Step Guide 🗂 Category: DATA SCIENCE 🕒 Date: 2025-03-28 | ⏱️ Read time: 17 min read Learn the complete 3D reconstruction pipeline from feature extraction to dense matching. Master photogrammetry with…

📌 A Little More Conversation, A Little Less Action — A Case Against Premature Data Integration 🗂 Category: DATA SCIENCE 🕒
📌 A Little More Conversation, A Little Less Action — A Case Against Premature Data Integration 🗂 Category: DATA SCIENCE 🕒 Date: 2025-03-28 | ⏱️ Read time: 14 min read Running a large data integration project before embarking on the ML part is easily a…

📌 The Art of Hybrid Architectures 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-28 | ⏱️ Read time: 32 min read Combi
📌 The Art of Hybrid Architectures 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-28 | ⏱️ Read time: 32 min read Combining CNNs and Transformers to Elevate Fine-Grained Visual Classification

📌 Understanding the Tech Stack Behind Generative AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-31 | ⏱️ Read time:
📌 Understanding the Tech Stack Behind Generative AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-31 | ⏱️ Read time: 22 min read From foundation models to vector databases and AI agents — what makes modern AI work