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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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📈 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 40 373 suscriptores, ocupando la posición 3 327 en la categoría Tecnologías y Aplicaciones y el puesto 225 en la región Siria.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 373 suscriptores.

Según los últimos datos del 12 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 399, y en las últimas 24 horas de 24, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.42%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.74% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 979 visualizaciones. En el primer día suele acumular 703 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 4.
  • 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 13 julio, 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.

40 373
Suscriptores
+2424 horas
+1257 días
+39930 días
Archivo de publicaciones
📌 A Visual Guide to Tuning Random Forest Hyperparameters 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-04 | ⏱️ Read time: 8 min
📌 A Visual Guide to Tuning Random Forest Hyperparameters 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-04 | ⏱️ Read time: 8 min read How hyperparameter tuning visually changes random forests

📌 Should We Use LLMs As If They Were Swiss Knives? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-04 | ⏱️ Read time:
📌 Should We Use LLMs As If They Were Swiss Knives? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-04 | ⏱️ Read time: 9 min read A logic game performance comparison between popular LLMs and a custom-made algorithm

📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05 | ⏱️ Read time: 16 min read Tool masking fo
📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05 | ⏱️ Read time: 16 min read Tool masking for AI improves AI agents: shape MCP tool surfaces to cut tokens and…

📌 Zero-Inflated Data: A Comparison of Regression Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-05 | ⏱️ Read time: 13 min
📌 Zero-Inflated Data: A Comparison of Regression Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-05 | ⏱️ Read time: 13 min read How to detect it and which model to choose.

📌 AI Operations Under the Hood: Challenges and Best Practices 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-09-05 | ⏱️ Read ti
📌 AI Operations Under the Hood: Challenges and Best Practices 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-09-05 | ⏱️ Read time: 18 min read Building robust, reproducible, and reliable GenAI applications requires a framework of continuous improvement, rigorous evaluation,…

📌 Showcasing Your Work on HuggingFace Spaces 🗂 Category: PRODUCTIVITY 🕒 Date: 2025-09-05 | ⏱️ Read time: 9 min read Buildi
📌 Showcasing Your Work on HuggingFace Spaces 🗂 Category: PRODUCTIVITY 🕒 Date: 2025-09-05 | ⏱️ Read time: 9 min read Building an app is exciting – but sharing it is where the real value kicks…

📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05 |
📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05 | ⏱️ Read time: 9 min read Learn how to apply context engineering to enhance your question answering systems.

📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05T07:00:00-05:00 | ⏱ Read time: 16 min read T
📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05T07:00:00-05:00 | ⏱ Read time: 16 min read Tool masking for AI improves AI agents: shape MCP tool surfaces to cut tokens and…

📌 Zero-Inflated Data: A Comparison of Regression Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-05T08:30:00-05:00 | ⏱ Rea
📌 Zero-Inflated Data: A Comparison of Regression Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-05T08:30:00-05:00 | ⏱ Read time: 13 min read How to detect it and which model to choose.

📌 AI Operations Under the Hood: Challenges and Best Practices 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-09-05T10:00:00-05:
📌 AI Operations Under the Hood: Challenges and Best Practices 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-09-05T10:00:00-05:00 | ⏱ Read time: 18 min read Building robust, reproducible, and reliable GenAI applications requires a framework of continuous improvement, rigorous evaluation,…

📌 Showcasing Your Work on HuggingFace Spaces 🗂 Category: PRODUCTIVITY 🕒 Date: 2025-09-05T11:30:00-05:00 | ⏱ Read time: 9 m
📌 Showcasing Your Work on HuggingFace Spaces 🗂 Category: PRODUCTIVITY 🕒 Date: 2025-09-05T11:30:00-05:00 | ⏱ Read time: 9 min read Building an app is exciting – but sharing it is where the real value kicks…

📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05T12
📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05T12:30:00-05:00 | ⏱ Read time: 9 min read Learn how to apply context engineering to enhance your question answering systems.

🔥 Trending Repository: ML-From-Scratch 📝 Description: Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning. 🔗 Repository URL: https://github.com/eriklindernoren/ML-From-Scratch 📖 Readme: https://github.com/eriklindernoren/ML-From-Scratch#readme 📊 Statistics: 🌟 Stars: 27.8K stars 👀 Watchers: 951 🍴 Forks: 4.8K forks 💻 Programming Languages: Python 🏷️ Related Topics:
#data_science #machine_learning #data_mining #deep_learning #genetic_algorithm #deep_reinforcement_learning #machine_learning_from_scratch
================================== 🧠 By: https://t.me/DataScienceM

No one believed I could double my portfolio in just 30 days… but then I discovered the Prop Mastery tricks top traders keep s
No one believed I could double my portfolio in just 30 days… but then I discovered the Prop Mastery tricks top traders keep secret. Want to know how? The real strategies are hidden right here — and only a few catch on before it’s too late! #إعلان InsideAds

“I was laughed at when I bought crypto in 2019. Now my portfolio’s up 1200% — and friends keep asking for ‘the secret’… But n
“I was laughed at when I bought crypto in 2019. Now my portfolio’s up 1200% — and friends keep asking for ‘the secret’… But nobody talks about the brutal truths I learned along the way. Want to see what everyone’s missing? 👉 See it here #إعلان InsideAds

Think you know your football history? Who scored a hat-trick against Real Madrid in yellow and black? Every day, Footy Riddle
Think you know your football history? Who scored a hat-trick against Real Madrid in yellow and black? Every day, Footy Riddles ⚽ drops clever puzzles about iconic players, unforgettable moments, and legendary matches. Challenge your football brain and see if you can guess before anyone else! Only the quickest fans know all the answers—are you one of them? #إعلان InsideAds

90% трейдеров сливают из-за ручного анализа и эмоций? TRUE — первый AI-протокол на Solana, который сам учится на on-chain дан
90% трейдеров сливают из-за ручного анализа и эмоций? TRUE — первый AI-протокол на Solana, который сам учится на on-chain данных, выдает персональные стратегии и делает трейдинг понятным на естественном языке. Именно сейчас можно получить доступ к запуску и участвовать в токенсейле $TRUE, пока условия максимально выгодные. Трейдинг будущего начинается здесь — не упусти шанс быть первым! Присоединяйся #إعلان InsideAds

What if you could skip all the chart noise and just ask AI to invest for you? 90% of traders lose money… but the game changes
What if you could skip all the chart noise and just ask AI to invest for you? 90% of traders lose money… but the game changes when you chat instead of chart. Be early—join the AI revolution before everyone else wakes up. #إعلان InsideAds

🔥 Trending Repository: abogen 📝 Description: Generate audiobooks from EPUBs, PDFs and text with synchronized captions. 🔗 R
🔥 Trending Repository: abogen 📝 Description: Generate audiobooks from EPUBs, PDFs and text with synchronized captions. 🔗 Repository URL: https://github.com/denizsafak/abogen 🌐 Website: https://pypi.org/project/abogen/ 📖 Readme: https://github.com/denizsafak/abogen#readme 📊 Statistics: 🌟 Stars: 3.1K stars 👀 Watchers: 18 🍴 Forks: 159 forks 💻 Programming Languages: Python - Batchfile - Dockerfile 🏷️ Related Topics:
#text_to_speech #audiobook #tts #speech_synthesis #subtitles #audiobooks #narrator #content_creator #voice_synthesis #epub_converter #kokoro #content_creation #text_to_audio #media_generation #kokoro_tts #kokoro_82m
================================== 🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: cognitive-load 📝 Description: 🧠 Cognitive Load is what matters 🔗 Repository URL: https://github.co
🔥 Trending Repository: cognitive-load 📝 Description: 🧠 Cognitive Load is what matters 🔗 Repository URL: https://github.com/zakirullin/cognitive-load 📖 Readme: https://github.com/zakirullin/cognitive-load#readme 📊 Statistics: 🌟 Stars: 9.4K stars 👀 Watchers: 86 🍴 Forks: 200 forks 💻 Programming Languages: Not available 🏷️ Related Topics: Not available ================================== 🧠 By: https://t.me/DataScienceM