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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 40 208 suscriptores, ocupando la posición 3 344 en la categoría Tecnologías y Aplicaciones y el puesto 228 en la región Siria.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 208 suscriptores.

Según los últimos datos del 03 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 338, y en las últimas 24 horas de 9, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.04%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.42% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 822 visualizaciones. En el primer día suele acumular 973 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
  • 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 04 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.

40 208
Suscriptores
+924 horas
+727 días
+33830 días
Archivo de publicaciones
📌 AI Agents: The Intersection of Tool Calling and Reasoning in Generative AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2
📌 AI Agents: The Intersection of Tool Calling and Reasoning in Generative AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-05 | ⏱️ Read time: 13 min read Unpacking problem solving and tool-driven decision making in AI

📌 How I Turned IPL Stats into a Mesmerizing Bar Chart Race 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-06 | ⏱️ Read time: 8 m
📌 How I Turned IPL Stats into a Mesmerizing Bar Chart Race 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-06 | ⏱️ Read time: 8 min read A step-by-step guide to creating captivating animated visualizations for data storytelling

📌 The Rise of Pallas: Unlocking TPU Potential with Custom Kernels 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-06 |
📌 The Rise of Pallas: Unlocking TPU Potential with Custom Kernels 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-06 | ⏱️ Read time: 17 min read Accelerating AI/ML Model Training with Custom Operators – Part 3

📌 FormulaFeatures: A Tool to Generate Highly Predictive Features for Interpretable Models 🗂 Category: 🕒 Date: 2024-10-06 |
📌 FormulaFeatures: A Tool to Generate Highly Predictive Features for Interpretable Models 🗂 Category: 🕒 Date: 2024-10-06 | ⏱️ Read time: 41 min read Create more interpretable models by using concise, highly predictive features, automatically engineered based on arithmetic…

📌 Exploring the AI Alignment Problem with GridWorlds 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-06 | ⏱️ Read time
📌 Exploring the AI Alignment Problem with GridWorlds 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-06 | ⏱️ Read time: 25 min read It’s difficult to build capable AI agents without encountering orthogonal goals

📌 How Did Open Food Facts Fix OCR-Extracted Ingredients Using Open-Source LLMs? 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-
📌 How Did Open Food Facts Fix OCR-Extracted Ingredients Using Open-Source LLMs? 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-06 | ⏱️ Read time: 15 min read Delve into an end-to-end Machine Learning project to improve the quality of the Open Food…

📌 Getting Started with Powerful Data Tables in your Python Web Apps 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-06 | ⏱️ Read
📌 Getting Started with Powerful Data Tables in your Python Web Apps 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-06 | ⏱️ Read time: 6 min read Using AG Grid to build a Finance app in pure Python with Reflex

📌 Top 5 Geospatial Data APIs for Advanced Analysis 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-06 | ⏱️ Read time: 22 min read
📌 Top 5 Geospatial Data APIs for Advanced Analysis 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-06 | ⏱️ Read time: 22 min read Explore Overpass, Geoapify, Distancematrix.ai, Amadeus, and Mapillary for Advanced Mapping and Location Data

📌 Arrays – Data Structures & Algorithms for Data Scientists 🗂 Category: CODING 🕒 Date: 2024-10-07 | ⏱️ Read time: 6 min re
📌 Arrays – Data Structures & Algorithms for Data Scientists 🗂 Category: CODING 🕒 Date: 2024-10-07 | ⏱️ Read time: 6 min read How dynamic and static arrays work under the hood

📌 Discover AWS Lambda Basics to Run Powerful Serverless Functions 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-07 |
📌 Discover AWS Lambda Basics to Run Powerful Serverless Functions 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-07 | ⏱️ Read time: 12 min read Learn how I navigated setting up AWS Lambda for the first time

📌 AlphaFold 2 Through the Context of BERT 🗂 Category: 🕒 Date: 2024-10-07 | ⏱️ Read time: 9 min read Understanding AI appli
📌 AlphaFold 2 Through the Context of BERT 🗂 Category: 🕒 Date: 2024-10-07 | ⏱️ Read time: 9 min read Understanding AI applications in bio for machine learning engineers

📌 Using Linear Equations + LLM to Solve LinkedIn Queens Game 🗂 Category: 🕒 Date: 2024-10-07 | ⏱️ Read time: 11 min read Pr
📌 Using Linear Equations + LLM to Solve LinkedIn Queens Game 🗂 Category: 🕒 Date: 2024-10-07 | ⏱️ Read time: 11 min read Prompting GPT to form and solve the linear equations using PuLP

📌 Scaling RAG from POC to Production 🗂 Category: CHATGPT 🕒 Date: 2024-10-07 | ⏱️ Read time: 8 min read Common challenges a
📌 Scaling RAG from POC to Production 🗂 Category: CHATGPT 🕒 Date: 2024-10-07 | ⏱️ Read time: 8 min read Common challenges and architectural components to enable scaling

📌 K Nearest Neighbor Regressor, Explained: A Visual Guide with Code Examples 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-07 |
📌 K Nearest Neighbor Regressor, Explained: A Visual Guide with Code Examples 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-07 | ⏱️ Read time: 11 min read Finding the neighbors FAST with KD Trees and Ball Trees

📌 Supercharge Your LLM Apps using DSPy and Langfuse 🗂 Category: NATURAL LANGUAGE PROCESSING 🕒 Date: 2024-10-07 | ⏱️ Read t
📌 Supercharge Your LLM Apps using DSPy and Langfuse 🗂 Category: NATURAL LANGUAGE PROCESSING 🕒 Date: 2024-10-07 | ⏱️ Read time: 14 min read Build Production Grade LLM Apps with Ease

📌 Implementing Sequential Algorithms on TPU 🗂 Category: 🕒 Date: 2024-10-07 | ⏱️ Read time: 13 min read Accelerating AI/ML
📌 Implementing Sequential Algorithms on TPU 🗂 Category: 🕒 Date: 2024-10-07 | ⏱️ Read time: 13 min read Accelerating AI/ML Model Training with Custom Operators – Part 3.A

📌 How to Talk About Data and Analysis Simply 🗂 Category: 🕒 Date: 2024-10-08 | ⏱️ Read time: 21 min read So that it is unde
📌 How to Talk About Data and Analysis Simply 🗂 Category: 🕒 Date: 2024-10-08 | ⏱️ Read time: 21 min read So that it is understandable and engaging to (almost) everyone

📌 Pandora’s Cloud Migration: Conquer the 7 “Bringers of Evil” 🗂 Category: 🕒 Date: 2024-10-08 | ⏱️ Read time: 20 min read A
📌 Pandora’s Cloud Migration: Conquer the 7 “Bringers of Evil” 🗂 Category: 🕒 Date: 2024-10-08 | ⏱️ Read time: 20 min read A guide to conquering cloud migration challenges

📌 Adding Gradient Backgrounds to Plotly Charts 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-08 | ⏱️ Read time: 5 min read Usin
📌 Adding Gradient Backgrounds to Plotly Charts 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-08 | ⏱️ Read time: 5 min read Using Plotly rectangle shapes to improve data visualisation

📌 Precisely Compare Geographical Regions with GeoPandas 🗂 Category: 🕒 Date: 2024-10-08 | ⏱️ Read time: 9 min read Filling
📌 Precisely Compare Geographical Regions with GeoPandas 🗂 Category: 🕒 Date: 2024-10-08 | ⏱️ Read time: 9 min read Filling maps with area measurements