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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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Red:📈 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 40 244 suscriptores, ocupando la posición 3 343 en la categoría Tecnologías y Aplicaciones y el puesto 227 en la región Siria.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 244 suscriptores.
Según los últimos datos del 05 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 346, y en las últimas 24 horas de 22, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.97%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.86% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 794 visualizaciones. En el primer día suele acumular 749 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 06 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.
40 244
Suscriptores
+2224 horas
+987 días
+34630 días
Archivo de publicaciones
40 244
📌 Building a Research Agent That Can Write to Google Docs (Part 1)
🗂 Category:
🕒 Date: 2024-11-20 | ⏱️ Read time: 18 min read
A tool that might help with your homework
40 244
📌 Building a Research Assistant That Can Write to Google Docs (Part 2)
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-11-20 | ⏱️ Read time: 14 min read
A tool that might help with your homework
40 244
📌 Your Data Quality Checks Are Worth Less (Than You Think)
🗂 Category: ANALYTICS
🕒 Date: 2024-11-20 | ⏱️ Read time: 6 min read
How to deliver outsized value on your data quality program
40 244
📌 Boost Your Python Code with CUDA
🗂 Category: DATA SCIENCE
🕒 Date: 2024-11-20 | ⏱️ Read time: 12 min read
Target your GPU easily with Numba’s CUDA JIT
40 244
📌 How to Build a Data-Driven Customer Management System
🗂 Category: BUSINESS
🕒 Date: 2024-11-20 | ⏱️ Read time: 16 min read
A High-Level Framework for Building a CBM System with Strategic Impact
40 244
📌 Is ReFT All We Needed?
🗂 Category: DEEP LEARNING
🕒 Date: 2024-11-21 | ⏱️ Read time: 6 min read
Representation Fintuning – Beyond the PEFT Techniques for fine-tuning LLMs
40 244
📌 How to Easily Deploy a Local Generative Search Engine Using VerifAI
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2024-11-21 | ⏱️ Read time: 10 min read
An open-source initiative to help you deploy generative search based on your local files and…
40 244
📌 Getting Started with Multimodal AI, One-Hot Encoding, and Other Beginner-Friendly Guides
🗂 Category: DATA SCIENCE
🕒 Date: 2024-11-21 | ⏱️ Read time: 5 min read
Our weekly selection of must-read Editors’ Picks and original features
40 244
📌 Explaining LLMs for RAG and Summarization
🗂 Category: DATA SCIENCE
🕒 Date: 2024-11-21 | ⏱️ Read time: 9 min read
A fast and low-resource method using similarity-based attribution
40 244
📌 Building a Vision Inspection CNN for an Industrial Application
🗂 Category:
🕒 Date: 2024-11-21 | ⏱️ Read time: 34 min read
A step-by-step approach using PyTorch
40 244
📌 Cluster While Predict: Iterative Methods for Regression and Classification
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-11-21 | ⏱️ Read time: 11 min read
Predictive and prescriptive analytics to bridge the gap between segmentation and prediction for real-world applications
40 244
📌 Leverage Python Inheritance in ML projects
🗂 Category: DATA SCIENCE
🕒 Date: 2024-11-21 | ⏱️ Read time: 6 min read
Learn how to implement coding best practices to avoid tech debts
40 244
📌 The SyncNet Research Paper, Clearly Explained
🗂 Category: DEEP LEARNING
🕒 Date: 2025-09-20 | ⏱️ Read time: 11 min read
A Deep Dive into “Out of Time: Automated Lip Sync in the Wild”
40 244
📌 Building LLM Apps That Can See, Think, and Integrate: Using o3 with Multimodal Input and Structured Output
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-09-20 | ⏱️ Read time: 12 min read
A hands-on example of building a time-series anomaly detection system entirely through visualization and prompting
40 244
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40 244
📌 Graph Neural Networks: Fraud Detection and Protein Function Prediction
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-11-21 | ⏱️ Read time: 8 min read
Understanding AI applications in bio for machine learning engineers
40 244
📌 ChatGPT: Two Years Later
🗂 Category: CHATGPT
🕒 Date: 2024-11-21 | ⏱️ Read time: 25 min read
Tracing the impact of the generative AI revolution
40 244
📌 Dynamic Visualizations in Python
🗂 Category:
🕒 Date: 2024-11-21 | ⏱️ Read time: 8 min read
How to animate plots with OpenCV and Matplotlib
40 244
📌 Neuromorphic Computing – an Edgier, Greener AI
🗂 Category: DEEP LEARNING
🕒 Date: 2024-11-22 | ⏱️ Read time: 18 min read
There are periodic proclamations of the coming neuromorphic computing revolution. While there remain substantial challenges…
40 244
📌 NuCS: A Constraint Solver for Research, Teaching, and Production Applications
🗂 Category: DATA SCIENCE
🕒 Date: 2024-11-22 | ⏱️ Read time: 6 min read
Blazing-fast constraint solving in pure Python
