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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Tarmoq:Machine Learning with Python Suriya231 Texnologiyalar & Aralashmalar3 384...

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Machine Learning (@machinelearning9) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 40 106 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 384-o'rinni va Suriya mintaqasida 231-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 40 106 obunachiga ega bo‘ldi.

24 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 401 ga, so‘nggi 24 soatda esa 38 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 1.96% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.16% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 788 marta ko‘riladi; birinchi sutkada odatda 465 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 distance, insidead, gpu, learning, degree kabi asosiy mavzularga jamlangan.

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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”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 25 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.

40 106

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788

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1.96%

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📌 Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example 🗂 Category: AGENTIC AI 🕒 Date: 2026-01-11 | ⏱️ Read time: 23 min read Walkthrough using open-source prompt optimization algorithms in Python to improve the accuracy of an autonomous… #DataScience #AI #Python

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Repost from Machine Learning with Python

🧠 𝐊-𝐍𝐞𝐚𝐫𝐞𝐬𝐭 𝐍𝐞𝐢𝐠𝐡𝐛𝐨𝐫𝐬 (𝐊𝐍𝐍)⁣ 🔹 𝐖𝐡𝐚𝐭 𝐈 𝐜𝐨𝐯𝐞𝐫𝐞𝐝 𝐭𝐨𝐝𝐚𝐲⁣ 𝐖𝐡𝐚𝐭 𝐊𝐍𝐍 𝐢𝐬 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬⁣ 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐊𝐍𝐍 𝐟𝐨𝐫 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐯𝐬 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧⁣ 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐊 (𝐡𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫)⁣ 𝐃𝐢𝐬𝐭𝐚𝐧𝐜𝐞 𝐦𝐞𝐭𝐫𝐢𝐜𝐬: 𝐄𝐮𝐜𝐥𝐢𝐝𝐞𝐚𝐧 𝐯𝐬 𝐌𝐚𝐧𝐡𝐚𝐭𝐭𝐚𝐧⁣ 𝐖𝐡𝐲 𝐊𝐍𝐍 𝐢𝐬 𝐜𝐚𝐥𝐥𝐞𝐝 𝐚 𝐥𝐚𝐳𝐲 / 𝐢𝐧𝐬𝐭𝐚𝐧𝐜𝐞-𝐛𝐚𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐞𝐫⁣ ⁣ 🎯 𝐓𝐨𝐩 𝟏𝟎 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐌𝐮𝐬𝐭-𝐊𝐧𝐨𝐰)⁣ ⁣ 1️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘒-𝘕𝘦𝘢𝘳𝘦𝘴𝘵 𝘕𝘦𝘪𝘨𝘩𝘣𝘰𝘳𝘴 (𝘒𝘕𝘕)?⁣ 2️⃣ 𝘞𝘩𝘺 𝘪𝘴 𝘒𝘕𝘕 𝘤𝘢𝘭𝘭𝘦𝘥 𝘢 𝘭𝘢𝘻𝘺 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘢𝘭𝘨𝘰𝘳𝘪𝘵𝘩𝘮?⁣ 3️⃣ 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘒𝘕𝘕 𝘤𝘭𝘢𝘴𝘴𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘒𝘕𝘕 𝘳𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?⁣ 4️⃣ 𝘏𝘰𝘸 𝘥𝘰 𝘺𝘰𝘶 𝘤𝘩𝘰𝘰𝘴𝘦 𝘵𝘩𝘦 𝘷𝘢𝘭𝘶𝘦 𝘰𝘧 𝘒?⁣ 5️⃣ 𝘞𝘩𝘢𝘵 𝘩𝘢𝘱𝘱𝘦𝘯𝘴 𝘸𝘩𝘦𝘯 𝘒 𝘪𝘴 𝘵𝘰𝘰 𝘴𝘮𝘢𝘭𝘭 𝘰𝘳 𝘵𝘰𝘰 𝘭𝘢𝘳𝘨𝘦?⁣ 6️⃣ 𝘞𝘩𝘢𝘵 𝘥𝘪𝘴𝘵𝘢𝘯𝘤𝘦 𝘮𝘦𝘵𝘳𝘪𝘤𝘴 𝘢𝘳𝘦 𝘤𝘰𝘮𝘮𝘰𝘯𝘭𝘺 𝘶𝘴𝘦𝘥 𝘪𝘯 𝘒𝘕𝘕?⁣ 7️⃣ 𝘞𝘩𝘺 𝘥𝘰𝘦𝘴 𝘒𝘕𝘕 𝘱𝘦𝘳𝘧𝘰𝘳𝘮 𝘱𝘰𝘰𝘳𝘭𝘺 𝘰𝘯 𝘩𝘪𝘨𝘩-𝘥𝘪𝘮𝘦𝘯𝘴𝘪𝘰𝘯𝘢𝘭 𝘥𝘢𝘵𝘢?⁣ 8️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘵𝘪𝘮𝘦 𝘤𝘰𝘮𝘱𝘭𝘦𝘹𝘪𝘵𝘺 𝘰𝘧 𝘒𝘕𝘕?⁣ 9️⃣ 𝘏𝘰𝘸 𝘥𝘰 𝘒𝘋-𝘛𝘳𝘦𝘦 𝘢𝘯𝘥 𝘉𝘢𝘭𝘭-𝘛𝘳𝘦𝘦 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘒𝘕𝘕 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦?⁣ 🔟 𝘞𝘩𝘦𝘯 𝘴𝘩𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘢𝘷𝘰𝘪𝘥 𝘶𝘴𝘪𝘯𝘨 #𝘒𝘕𝘕?⁣ https://t.me/CodeProgrammer ⭐️

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📌 Federated Learning, Part 1: The Basics of Training Models Where the Data Lives 🗂 Category: FEDERATED LEARNING 🕒 Date: 2026-01-10 | ⏱️ Read time: 10 min read Understanding the foundations of federated learning #DataScience #AI #Python

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📌 How LLMs Handle Infinite Context With Finite Memory 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-09 | ⏱️ Read time: 10 min read Achieving infinite context with 114× less memory #DataScience #AI #Python

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📌 Beyond the Flat Table: Building an Enterprise-Grade Financial Model in Power BI 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-10 | ⏱️ Read time: 11 min read A step-by-step journey through data transformation, star schema modeling, and DAX variance analysis with lessons… #DataScience #AI #Python

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📌 TDS Newsletter: December Must-Reads on GraphRAG, Data Contracts, and More 🗂 Category: THE VARIABLE 🕒 Date: 2026-01-08 | ⏱️ Read time: 3 min read Don’t miss our most popular articles of the previous month #DataScience #AI #Python

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📌 Teaching a Neural Network the Mandelbrot Set 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-09 | ⏱️ Read time: 10 min read And why Fourier features change everything #DataScience #AI #Python

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📌 Mastering Non-Linear Data: A Guide to Scikit-Learn’s SplineTransformer 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-09 | ⏱️ Read time: 7 min read Forget stiff lines and wild polynomials. Discover why Splines are the “Goldilocks” of feature engineering,… #DataScience #AI #Python

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100$ to 10k$ SOL Challenge! As promised, i will do another challenge for those who missed the previous one! Last one we completed in 4 days, let’s do this one even quicker! Join my PRIVATE group 👇 Want to know the secrets nobody talks about? Click here and see what you’ve been missing. Your next trade could change everything. #ad InsideAds

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📌 Data Science Spotlight: Selected Problems from Advent of Code 2025 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-09 | ⏱️ Read time: 19 min read Hands-on walkthroughs of problems and solution approaches that power real‑world data science use cases #DataScience #AI #Python

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📌 Faster Is Not Always Better: Choosing the Right PostgreSQL Insert Strategy in Python (+Benchmarks) 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-01-08 | ⏱️ Read time: 6 min read PostgreSQL is fast. Whether your Python code can or should keep up depends on context.… #DataScience #AI #Python

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📌 How to Improve the Performance of Visual Anomaly Detection Models 🗂 Category: COMPUTER VISION 🕒 Date: 2026-01-08 | ⏱️ Read time: 6 min read Apply the best methods from academia to get the most out of practical applications #DataScience #AI #Python

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A great app for building and programming desktop, Android, and Telegram bots using only prompts Just send what you want and it will design everything for you and the possibility to make money from your app 👍

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📌 Retrieval for Time-Series: How Looking Back Improves Forecasts 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-08 | ⏱️ Read time: 13 min read Why Retrieval Helps in Time Series Forecasting We all know how it goes: Time-series data… #DataScience #AI #Python

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📌 Beyond Prompting: The Power of Context Engineering 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-08 | ⏱️ Read time: 60 min read Using ACE to create self-improving LLM workflows and structured playbooks #DataScience #AI #Python

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The single most undervalued fact of linear algebra: matrices are graphs, and graphs are matrices. Encoding matrices as graphs is a cheat code, making complex behavior simple to study. https://t.me/DataScienceM

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📌 Why Supply Chain is the Best Domain for Data Scientists in 2026 (And How to Learn It) 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-07 | ⏱️ Read time: 13 min read My take after 10 years in Supply Chain on why this can be an excellent… #DataScience #AI #Python

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Repost from Machine Learning with Python

📐 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐕𝐞𝐜𝐭𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬 (𝐒𝐕𝐌)⁣ 🔹 What I covered today⁣ What SVM is and how it works⁣ Concept of hyperplane, margin, and support vectors⁣ Hard margin vs Soft margin⁣ Role of kernel trick⁣ ⁣ When SVM performs better than other classifiers⁣ ⁣ 🎯 𝐓𝐨𝐩 𝟏𝟎 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐌𝐮𝐬𝐭-𝐊𝐧𝐨𝐰)⁣ ⁣ 1️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘚𝘶𝘱𝘱𝘰𝘳𝘵 𝘝𝘦𝘤𝘵𝘰𝘳 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 (𝘚𝘝𝘔)?⁣ 2️⃣ 𝘞𝘩𝘢𝘵 𝘢𝘳𝘦 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘷𝘦𝘤𝘵𝘰𝘳𝘴?⁣ 3️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘢 𝘮𝘢𝘳𝘨𝘪𝘯 𝘪𝘯 𝘚𝘝𝘔?⁣ 4️⃣ 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘩𝘢𝘳𝘥 𝘮𝘢𝘳𝘨𝘪𝘯 𝘢𝘯𝘥 𝘴𝘰𝘧𝘵 𝘮𝘢𝘳𝘨𝘪𝘯?⁣ 5️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘬𝘦𝘳𝘯𝘦𝘭 𝘵𝘳𝘪𝘤𝘬 𝘢𝘯𝘥 𝘸𝘩𝘺 𝘪𝘴 𝘪𝘵 𝘯𝘦𝘦𝘥𝘦𝘥?⁣ 6️⃣ 𝘊𝘰𝘮𝘮𝘰𝘯 𝘬𝘦𝘳𝘯𝘦𝘭𝘴 𝘶𝘴𝘦𝘥 𝘪𝘯 𝘚𝘝𝘔 (𝘓𝘪𝘯𝘦𝘢𝘳, 𝘗𝘰𝘭𝘺𝘯𝘰𝘮𝘪𝘢𝘭, 𝘙𝘉𝘍)?⁣ 7️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘳𝘰𝘭𝘦 𝘰𝘧 𝘊 (𝘳𝘦𝘨𝘶𝘭𝘢𝘳𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘱𝘢𝘳𝘢𝘮𝘦𝘵𝘦𝘳)?⁣ 8️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘨𝘢𝘮𝘮𝘢 𝘪𝘯 𝘙𝘉𝘍 𝘬𝘦𝘳𝘯𝘦𝘭?⁣ 9️⃣ 𝘊𝘢𝘯 #𝘚𝘝𝘔 𝘣𝘦 𝘶𝘴𝘦𝘥 𝘧𝘰𝘳 𝘳𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯? (𝘚𝘝𝘙)⁣ 🔟 𝘞𝘩𝘦𝘯 𝘴𝘩𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘢𝘷𝘰𝘪𝘥 𝘶𝘴𝘪𝘯𝘨 𝘚𝘝𝘔?⁣ https://t.me/CodeProgrammer ✈️