¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
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
Mostrar más📈 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 40 140 suscriptores, ocupando la posición 3 371 en la categoría Tecnologías y Aplicaciones y el puesto 230 en la región Siria.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 140 suscriptores.
Según los últimos datos del 26 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 429, y en las últimas 24 horas de 20, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.83%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.60% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 735 visualizaciones. En el primer día suele acumular 643 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 27 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.
40 140
Suscriptores
+2024 horas
+1017 días
+42930 días
Archivo de publicaciones
40 140
📌 How to Automate Workflows with AI
🗂 Category: AGENTIC AI
🕒 Date: 2025-11-15 | ⏱️ Read time: 7 min read
Unlock the power of AI to streamline your operations. This guide details how to transform tedious manual processes into intelligent, automated workflows. Learn to identify key opportunities, select the right tools, and implement effective solutions to boost efficiency, reduce errors, and drive business innovation.
#AI #WorkflowAutomation #ProcessOptimization
40 140
🏆 Crack ML System Design Interviews
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40 140
📌 How to Crack Machine Learning System-Design Interviews
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-14 | ⏱️ Read time: 15 min read
Ace your machine learning system design interviews at top tech companies. This comprehensive guide provides a deep dive into the interview process at Meta, Apple, Reddit, Amazon, Google, and Snap, equipping you with the strategies needed to succeed in these high-stakes technical assessments.
#MachineLearning #SystemDesign #TechInterview #AI
40 140
📌 “The success of an AI product depends on how intuitively users can interact with its capabilities”
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-11-14 | ⏱️ Read time: 8 min read
Expert Janna Lipenkova emphasizes that the success of AI products hinges on intuitive user interaction, not just technological power. A winning AI strategy focuses on user-centric design, where deep domain knowledge is crucial for translating complex AI capabilities into accessible and valuable tools. This approach ensures that the product is not only intelligent but also seamlessly usable, defining the future of human-AI collaboration.
#AIUX #ProductManagement #AIStrategy #MachineLearning
40 140
📌 Critical Mistakes Companies Make When Integrating AI/ML into Their Processes
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-14 | ⏱️ Read time: 11 min read
Integrating AI/ML into business operations is a complex process where many companies falter. Based on insights from leading AI teams across various industries, this guide highlights the critical, yet common, mistakes organizations make during AI adoption. Learn to navigate pitfalls related to strategy, data quality, and implementation to ensure your machine learning initiatives succeed and deliver tangible business value, avoiding costly errors and maximizing your return on investment.
#AIIntegration #MachineLearning #AIStrategy #TechLeadership
40 140
📌 Music, Lyrics, and Agentic AI: Building a Smart Song Explainer using Python and OpenAI
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-11-14 | ⏱️ Read time: 10 min read
This is how to build an AI-powered Song Explainer using Python and OpenAI
#DataScience #AI #Python
40 140
📌 Spearman Correlation Coefficient for When Pearson Isn’t Enough
🗂 Category: DATA SCIENCE
🕒 Date: 2025-11-13 | ⏱️ Read time: 7 min read
Not all relationships are linear, and that is where Spearman comes in.
#DataScience #AI #Python
40 140
📌 Organizing Code, Experiments, and Research for Kaggle Competitions
🗂 Category: PROJECT MANAGEMENT
🕒 Date: 2025-11-13 | ⏱️ Read time: 21 min read
Winning a Kaggle medal requires a disciplined approach, not just a great model. This guide shares essential lessons and tips from a medalist on effectively organizing your code, tracking experiments, and structuring your research. Learn how to streamline your competitive data science workflow, avoid common pitfalls, and improve your chances of success.
#Kaggle #DataScience #MachineLearning #MLOps
40 140
📌 Robotics with Python: Q-Learning vs Actor-Critic vs Evolutionary Algorithms
🗂 Category: Uncategorized
🕒 Date: 2025-11-13 | ⏱️ Read time: 15 min read
Explore the intersection of Python and robotics in this deep dive into reinforcement learning algorithms. The article compares the trade-offs, strengths, and weaknesses of Q-Learning, Actor-Critic, and Evolutionary Algorithms for robotic control tasks. Learn how to apply these concepts by building a custom 3D environment to train and test your own RL-powered robot, providing a practical understanding of which technique to choose for your specific application.
#Python #Robotics #ReinforcementLearning #MachineLearning #AI
40 140
Eurasia 2025 Window Fair
15-18 November 2025 Istanbul Turkey Tüyap Fair Center Hall: 3 Stand: 308 A
At this importent event you will have the opportunity to feel the pulse of the industry
Sponsored By WaybienAds
40 140
📌 LLMs Are Randomized Algorithms
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-11-13 | ⏱️ Read time: 18 min read
A surprising link has been drawn between modern Large Language Models and the 50-year-old field of randomized algorithms. This perspective reframes LLMs not just as complex neural networks, but as a practical application of established algorithmic theory. Viewing today's most advanced AI through this lens offers a novel framework for analyzing their probabilistic nature, behavior, and underlying operational principles, bridging the gap between cutting-edge AI and foundational computer science.
#LLMs #AI #RandomizedAlgorithms #ComputerScience #MachineLearning
40 140
Eurasia 2025 Window Fair
15-18 November 2025 Istanbul Turkey Tüyap Fair Center Hall: 3 Stand: 308 A
At this importent event you will have the opportunity to feel the pulse of the industry
Sponsored By WaybienAds
40 140
Eurasia 2025 Window Fair
15-18 November 2025 Istanbul Turkey Tüyap Fair Center Hall: 3 Stand: 308 A
At this importent event you will have the opportunity to feel the pulse of the industry
Sponsored By WaybienAds
40 140
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40 140
Eurasia 2025 Window Fair
15-18 November 2025 Istanbul Turkey Tüyap Fair Center Hall: 3 Stand: 308 A
At this importent event you will have the opportunity to feel the pulse of the industry
Sponsored By WaybienAds
40 140
📌 Feature Detection, Part 2: Laplace & Gaussian Operators
🗂 Category: COMPUTER VISION
🕒 Date: 2025-11-12 | ⏱️ Read time: 12 min read
Laplace meets Gaussian — the story of two operators in edge detection
#DataScience #AI #Python
40 140
📌 How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-11-12 | ⏱️ Read time: 8 min read
This final part of the series on RAG pipeline evaluation explores advanced metrics for assessing retrieval quality. Learn how to use Discounted Cumulative Gain (DCG@k) and Normalized Discounted Cumulative Gain (NDCG@k) to measure the relevance and ranking of retrieved documents, moving beyond simpler metrics for a more nuanced understanding of your system's performance.
#RAG #EvaluationMetrics #LLM #InformationRetrieval #MLOps
40 140
📌 The Ultimate Guide to Power BI Aggregations
🗂 Category: DATA SCIENCE
🕒 Date: 2025-11-12 | ⏱️ Read time: 10 min read
Unlock significant performance gains in your Power BI reports by mastering aggregations. This guide explains how to leverage this powerful feature to optimize query performance and enhance user experience when working with massive datasets, enabling faster, more responsive analytics.
#PowerBI #DataModeling #BusinessIntelligence #BigData
40 140
📌 Deploy Your AI Assistant to Monitor and Debug n8n Workflows Using Claude and MCP
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-11-12 | ⏱️ Read time: 19 min read
Learn how to deploy an AI assistant powered by Claude and MCP to effectively monitor, analyze, and debug your n8n workflows. This innovative approach allows you to troubleshoot complex automations using natural language conversations, significantly streamlining your development and maintenance process.
#n8n #ClaudeAI #WorkflowAutomation #AIAssistant #Debugging
