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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
📌 Graph RAG into Production – step-by-step 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-23 | ⏱️ Read time: 17 min r
📌 Graph RAG into Production – step-by-step 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-23 | ⏱️ Read time: 17 min read A GCP native, fully serverless implementation that you will replicate in minutes

📌 Semantic Layer for the People and by the People 🗂 Category: 🕒 Date: 2024-09-23 | ⏱️ Read time: 14 min read My 3 +1 joker
📌 Semantic Layer for the People and by the People 🗂 Category: 🕒 Date: 2024-09-23 | ⏱️ Read time: 14 min read My 3 +1 jokers with templates for building a powerful analytical semantic layer

📌 Zero-Shot Localization with CLIP-Style Encoders 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-24 | ⏱️ Read time: 1
📌 Zero-Shot Localization with CLIP-Style Encoders 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-24 | ⏱️ Read time: 11 min read How can we see what a vision encoder sees?

📌 A Deep Dive into Odds Ratio 🗂 Category: STATISTICS 🕒 Date: 2024-09-24 | ⏱️ Read time: 20 min read Understanding, calcula
📌 A Deep Dive into Odds Ratio 🗂 Category: STATISTICS 🕒 Date: 2024-09-24 | ⏱️ Read time: 20 min read Understanding, calculating, visualizing, and interpreting odds ratios and their confidence intervals with practical examples in…

📌 Building an Interactive UI for Llamaindex Workflows 🗂 Category: 🕒 Date: 2024-09-24 | ⏱️ Read time: 11 min read A guide t
📌 Building an Interactive UI for Llamaindex Workflows 🗂 Category: 🕒 Date: 2024-09-24 | ⏱️ Read time: 11 min read A guide to integrating human-in-the-loop interactions using Llamaindex, FastAPI, and Streamlit

📌 Feature Engineering Techniques for Numerical Variables in Python 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-24 | ⏱️ Re
📌 Feature Engineering Techniques for Numerical Variables in Python 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-24 | ⏱️ Read time: 21 min read Learn the most useful feature engineering techniques to convert numerical values ​​into useful information for…

📌 I’ve hired 3 cohorts of data science interns – here’s my advice on getting an offer 🗂 Category: DATA SCIENCE 🕒 Date: 202
📌 I’ve hired 3 cohorts of data science interns – here’s my advice on getting an offer 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-24 | ⏱️ Read time: 16 min read Resume and interview tips for landing a data science internship

📌 Doctors Leverage Multimodal Data; Medical AI Should Too 🗂 Category: 🕒 Date: 2024-09-25 | ⏱️ Read time: 11 min read Integ
📌 Doctors Leverage Multimodal Data; Medical AI Should Too 🗂 Category: 🕒 Date: 2024-09-25 | ⏱️ Read time: 11 min read Integrating multimodal data enables a new generation of medical AI systems to better capture doctor’s…

📌 Water Cooler Small Talk: The Birthday Paradox 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-25 | ⏱️ Read time: 9 min read A l
📌 Water Cooler Small Talk: The Birthday Paradox 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-25 | ⏱️ Read time: 9 min read A look at the counterintuitive mathematics of shared birthdays

📌 Convenient Time Series Forecasting with sktime 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-25 | ⏱️ Read time: 8 min rea
📌 Convenient Time Series Forecasting with sktime 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-25 | ⏱️ Read time: 8 min read How to make forecasting as easy as a walk in the park

📌 Exposing Jailbreak Vulnerabilities in LLM Applications with ARTKIT 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-25 | ⏱️
📌 Exposing Jailbreak Vulnerabilities in LLM Applications with ARTKIT 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-25 | ⏱️ Read time: 10 min read Automated prompt-based testing to extract hidden passwords in the popular Gandalf challenge

📌 How Cohort Analysis Can Transform Your Customer Insights 🗂 Category: 🕒 Date: 2024-09-25 | ⏱️ Read time: 6 min read Disco
📌 How Cohort Analysis Can Transform Your Customer Insights 🗂 Category: 🕒 Date: 2024-09-25 | ⏱️ Read time: 6 min read Discover how tracking customer behavior over time with cohort analysis can improve engagement and retention…

📌 I Spent My Money on Benchmarking LLMs on Dutch Exams So You Don’t Have To 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024
📌 I Spent My Money on Benchmarking LLMs on Dutch Exams So You Don’t Have To 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-09-25 | ⏱️ Read time: 12 min read OpenAI’s new o1-preview is way too expensive for how it performs on the results

📌 VisionTS: Building Superior Forecasting Models from Images 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-26 | ⏱️ R
📌 VisionTS: Building Superior Forecasting Models from Images 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 9 min read Leveraging the power of images for time-series forecasting

📌 Simulate the Challenges of a Circular Economy for Fashion Retail 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read t
📌 Simulate the Challenges of a Circular Economy for Fashion Retail 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 17 min read Use data analytics to simulate a circular rental model for fashion retail and understand store…

📌 MCP in Practice 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-29 | ⏱️ Read time: 14 min read Mapping power, concentration, and
📌 MCP in Practice 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-29 | ⏱️ Read time: 14 min read Mapping power, concentration, and usage in the emerging AI developer ecosystem

📌 I Made My AI Model 84% Smaller and It Got Better, Not Worse 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-29 | ⏱️ Re
📌 I Made My AI Model 84% Smaller and It Got Better, Not Worse 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-29 | ⏱️ Read time: 20 min read The counterintuitive approach to AI optimization that’s changing how we deploy models

📌 Preparing Video Data for Deep Learning: Introducing Vid Prepper 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-09-29 | ⏱️ Rea
📌 Preparing Video Data for Deep Learning: Introducing Vid Prepper 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-09-29 | ⏱️ Read time: 13 min read A guide to fast video data preprocessing for machine learning

📌 Dummy Regressor, Explained: A Visual Guide with Code Examples for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26
📌 Dummy Regressor, Explained: A Visual Guide with Code Examples for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 7 min read Naively choosing the best number for all of your prediction

📌 Working with Embeddings: Closed versus Open Source 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-26 | ⏱️ Read time: 20 mi
📌 Working with Embeddings: Closed versus Open Source 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-26 | ⏱️ Read time: 20 min read Using techniques to improve semantic search