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 Siria225 Tecnologías y Aplicaciones3 331...

📈 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 40 345 suscriptores, ocupando la posición 3 331 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 40 345 suscriptores.

Según los últimos datos del 10 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 383, y en las últimas 24 horas de 25, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.35%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.95% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 948 visualizaciones. En el primer día suele acumular 786 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 11 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.

40 345

Suscriptores

+2524 horas

+1227 días

+38330 días

948

Visitas de la publicación

~ 78624 horas

~ 1 06648 horas

2.35%

Tasa de compromiso

~ 2

Mensajes por día

Ads index

beta

Archivo de publicaciones

40 334

📌 Will You Spot the Leaks? A Data Science Challenge 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-12 | ⏱️ Read time: 8 min read When models fly too high: A perilous journey through data leakage

40 334

📌 Running Python Programs in Your Browser 🗂 Category: PROGRAMMING 🕒 Date: 2025-05-12 | ⏱️ Read time: 17 min read Using Pyodide and Webassembly

40 334

📌 Pause Your ML Pipelines for Human Review Using AWS Step Functions + Slack 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-05-12 | ⏱️ Read time: 7 min read Build trust into your machine learning pipelines by inserting fast, secure human checks.

40 334

📌 The Westworld Blunder 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-12 | ⏱️ Read time: 16 min read Giving artificial minds the appearance of suffering without the awareness that it’s just a performance…

40 334

📌 How I Finally Understood MCP — and Got It Working in Real Life 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-12 | ⏱️ Read time: 28 min read The guide I needed when I had no idea why anyone would build an MCP…

40 334

📌 Empowering LLMs to Think Deeper by Erasing Thoughts 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-12 | ⏱️ Read time: 11 min read Introduction Recent large language models (LLMs) — such as OpenAI’s o1/o3, DeepSeek’s R1 and Anthropic’s…

40 334

📌 TDS Authors Can Now Receive Payments Via Stripe 🗂 Category: WRITING 🕒 Date: 2025-05-13 | ⏱️ Read time: 2 min read The Author Payment Program just became a lot more streamlined

40 334

📌 Rethinking the Environmental Costs of Training AI — Why We Should Look Beyond Hardware 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-13 | ⏱️ Read time: 19 min read A statistical analysis of what drives energy, water, and carbon consumption in AI training —…

40 334

📌 Non-Parametric Density Estimation: Theory and Applications 🗂 Category: STATISTICS 🕒 Date: 2025-05-13 | ⏱️ Read time: 26 min read A theoretical and practical introduction to non-parametric density estimation.

40 334

📌 Get Started with Rust: Installation and Your First CLI Tool – A Beginner’s Guide 🗂 Category: PROGRAMMING 🕒 Date: 2025-05-13 | ⏱️ Read time: 8 min read From setup to your first command line application — step by step

40 334

📌 Survival Analysis When No One Dies: A Value-Based Approach 🗂 Category: STATISTICS 🕒 Date: 2025-05-13 | ⏱️ Read time: 11 min read A generalized version of Kaplan-Meier allows to model a continuous value (like money) instead of…

40 334

📌 Parquet File Format – Everything You Need to Know! 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-05-14 | ⏱️ Read time: 9 min read New data flavors require new ways for storing it! Learn everything you need to know…

40 334

📌 Efficient Graph Storage for Entity Resolution Using Clique-Based Compression 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-14 | ⏱️ Read time: 7 min read Entity resolution systems face challenges with dense, interconnected graphs, and clique-based graph compression offers an…

40 334

📌 Strength in Numbers: Ensembling Models with Bagging and Boosting 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-15 | ⏱️ Read time: 16 min read Mastering the fundamentals of bagging and boosting with simple examples

40 334

📌 The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-05-15 | ⏱️ Read time: 8 min read A step closer to spatial AI with geospatial processing with Fabric

40 334

📌 Boost 2-Bit LLM Accuracy with EoRA 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-15 | ⏱️ Read time: 9 min read A training-free solution for extreme LLM compression.

40 334

📌 Explore the New World of Agent Protocols 🗂 Category: THE VARIABLE 🕒 Date: 2025-05-15 | ⏱️ Read time: 3 min read This week, we focus on helping you gain a deeper understanding of MCP and other…

40 334

📌 How to Learn the Math Needed for Machine Learning 🗂 Category: MATH 🕒 Date: 2025-05-15 | ⏱️ Read time: 7 min read A breakdown of the three fundamental math fields required for machine learning: statistics, linear algebra,…

40 334

📌 Understanding Random Forest using Python (scikit-learn) 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-05-15 | ⏱️ Read time: 9 min read A Random Forest is a powerful machine learning algorithm that can be used for classification…

40 334

📌 Google’s AlphaEvolve Is Evolving New Algorithms — And It Could Be a Game Changer 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-15 | ⏱️ Read time: 6 min read A blend of LLMs’ creative generation capabilities with genetic algorithms