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

Red:Machine Learning with Python Siria231 Tecnologías y Aplicaciones3 384...

📈 Análisis del canal de Telegram Machine Learning

El canal Machine Learning (@machinelearning9) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 40 106 suscriptores, ocupando la posición 3 384 en la categoría Tecnologías y Aplicaciones y el puesto 231 en la región Siria.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 106 suscriptores.

Según los últimos datos del 24 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 401, y en las últimas 24 horas de 38, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.96%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.16% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 788 visualizaciones. En el primer día suele acumular 465 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 2.
Intereses temáticos: El contenido se centra en temas clave como distance, insidead, gpu, learning, degree.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 25 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

40 106

Suscriptores

+3824 horas

+637 días

+40130 días

788

Visitas de la publicación

~ 46524 horas

~ 62048 horas

1.96%

Tasa de compromiso

~ 3

Mensajes por día

Ads index

beta

Archivo de publicaciones

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📌 Topic Modeling Techniques for 2026: Seeded Modeling, LLM Integration, and Data Summaries 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-14 | ⏱️ Read time: 15 min read Seeded topic modeling, integration with LLMs, and training on summarized data are the fresh parts… #DataScience #AI #Python

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📌 Glitches in the Attention Matrix 🗂 Category: DEEP LEARNING 🕒 Date: 2026-01-14 | ⏱️ Read time: 13 min read A history of Transformer artifacts and the latest research on how to fix them #DataScience #AI #Python

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Repost from Machine Learning with Python

Do you want to teach AI on real projects? In this #repository, there are 29 projects with Generative #AI,#MachineLearning, and #Deep +Learning. With full #code for each one. This is pure gold: https://github.com/KalyanM45/AI-Project-Gallery 👉 https://t.me/CodeProgrammer

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📌 What Is a Knowledge Graph — and Why It Matters 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-14 | ⏱️ Read time: 18 min read How structured knowledge became healthcare’s quiet advantage #DataScience #AI #Python

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📌 Why Human-Centered Data Analytics Matters More Than Ever 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-14 | ⏱️ Read time: 8 min read From optimizing metrics to designing meaning: putting people back into data-driven decisions #DataScience #AI #Python

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📌 From ‘Dataslows’ to Dataflows: The Gen2 Performance Revolution in Microsoft Fabric 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-01-13 | ⏱️ Read time: 8 min read Dataflows were (rightly?) considered “the slowest and least performant option” for ingesting data into Power… #DataScience #AI #Python

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📌 An introduction to AWS Bedrock 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-13 | ⏱️ Read time: 13 min read The how, why, what and where of Amazon’s LLM access layer #DataScience #AI #Python

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Repost from Machine Learning with Python

⚡️ All cheat sheets for programmers in one place. There's a lot of useful stuff inside: short, clear tips on languages, technologies, and frameworks. No registration required and it's free. https://overapi.com/ #python #php #Database #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS https://t.me/CodeProgrammer ⚡️

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📌 How to Maximize Claude Code Effectiveness 🗂 Category: AGENTIC AI 🕒 Date: 2026-01-13 | ⏱️ Read time: 9 min read Learn how to get the most out of agentic coding #DataScience #AI #Python

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📌 Why Your ML Model Works in Training But Fails in Production 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-13 | ⏱️ Read time: 8 min read Hard lessons from building production ML systems where data leaks, defaults lie, populations shift, and… #DataScience #AI #Python

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📌 Under the Uzès Sun: When Historical Data Reveals the Climate Change 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-13 | ⏱️ Read time: 11 min read Longer summers, milder winters: analysis of temperature trends in Uzès, France, year after year. #DataScience #AI #Python

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📌 Optimizing Data Transfer in Batched AI/ML Inference Workloads 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-01-12 | ⏱️ Read time: 13 min read A deep dive on data transfer bottlenecks, their identification, and their resolution with the help… #DataScience #AI #Python

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📌 When Does Adding Fancy RAG Features Work? 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-12 | ⏱️ Read time: 23 min read Looking at the performance of different pipelines #DataScience #AI #Python

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📌 Why 90% Accuracy in Text-to-SQL is 100% Useless 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-12 | ⏱️ Read time: 9 min read The eternal promise of self-service analytics #DataScience #AI #Python

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Repost from Machine Learning with Python

These Google Colab-notebooks help to implement all machine learning algorithms from scratch 🤯 Repo: https://udlbook.github.io/udlbook/ 👉 @codeprogrammer

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try this bot ai https://t.me/Creator_Ai_Tech_Bot?start=109697-ae026840

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📌 How AI Can Become Your Personal Language Tutor 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-12 | ⏱️ Read time: 11 min read How I used n8n to build AI study partners for learning Mandarin: vocabulary, listening, and… #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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📌 How to Leverage Slash Commands to Code Effectively 🗂 Category: LLM APPLICATIONS 🕒 Date: 2026-01-11 | ⏱️ Read time: 8 min read Learn how I utilize slash commands to be a more efficient engineer #DataScience #AI #Python