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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 40 221 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 40 221 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.

40 221
Suscriptores
+924 horas
+727 días
+33830 días
Archivo de publicaciones
📌 How to Choose the Best ML Deployment Strategy: Cloud vs. Edge 🗂 Category: 🕒 Date: 2024-10-14 | ⏱️ Read time: 17 min read
📌 How to Choose the Best ML Deployment Strategy: Cloud vs. Edge 🗂 Category: 🕒 Date: 2024-10-14 | ⏱️ Read time: 17 min read The choice between cloud and edge deployment could make or break your project

📌 Evaluating synthetic data 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-14 | ⏱️ Read time: 9 min read Assessing plausibil
📌 Evaluating synthetic data 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-14 | ⏱️ Read time: 9 min read Assessing plausibility and usefulness of data we generated from real data

📌 AI Feels Easier Than Ever, But Is It Really? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-15 | ⏱️ Read time: 9 mi
📌 AI Feels Easier Than Ever, But Is It Really? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-15 | ⏱️ Read time: 9 min read The 4 big challenges of building AI products

📌 I Built An AI Human-Level Game Player 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-15 | ⏱️ Read time: 13 min read
📌 I Built An AI Human-Level Game Player 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-15 | ⏱️ Read time: 13 min read Old-school game trees can be incredibly effective.

📌 Dataflow architecture 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-10-15 | ⏱️ Read time: 23 min read on derived data views
📌 Dataflow architecture 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-10-15 | ⏱️ Read time: 23 min read on derived data views and eventual consistency

📌 I Fine-Tuned the Tiny Llama 3.2 1B to Replace GPT-4o 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-15 | ⏱️ Read time: 8 min r
📌 I Fine-Tuned the Tiny Llama 3.2 1B to Replace GPT-4o 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-15 | ⏱️ Read time: 8 min read Is the fine-tuning effort worth more than few-shot prompting?

📌 Continual Learning: A Primer 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-15 | ⏱️ Read time: 8 min read Plus paper recommen
📌 Continual Learning: A Primer 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-15 | ⏱️ Read time: 8 min read Plus paper recommendations

📌 Normalized Discounted Cumulative Gain (NDCG) – The Ultimate Ranking Metric 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-15 |
📌 Normalized Discounted Cumulative Gain (NDCG) – The Ultimate Ranking Metric 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-15 | ⏱️ Read time: 10 min read NDCG – The Rank-Aware Metric for Evaluating Recommendation Systems

📌 Will Your Vote Decide the Next President? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-15 | ⏱️ Read time: 22 min read Simula
📌 Will Your Vote Decide the Next President? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-15 | ⏱️ Read time: 22 min read Simulating the probability that your singular vote swings the election in November

📌 Beyond Naive RAG: Advanced Techniques for Building Smarter and Reliable AI Systems 🗂 Category: LARGE LANGUAGE MODELS 🕒 D
📌 Beyond Naive RAG: Advanced Techniques for Building Smarter and Reliable AI Systems 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-10-16 | ⏱️ Read time: 32 min read A deep dive into advanced indexing, pre-retrieval, retrieval, and post-retrieval techniques to enhance RAG performance

📌 Marketing Mix Modeling (MMM): How to Avoid Biased Channel Estimates 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-16 | ⏱️ Rea
📌 Marketing Mix Modeling (MMM): How to Avoid Biased Channel Estimates 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-16 | ⏱️ Read time: 16 min read Learn which variables you should and should not take into account in your model.

📌 The Science Behind AI’s First Nobel Prize 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-16 | ⏱️ Read time: 13 min read Ho
📌 The Science Behind AI’s First Nobel Prize 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-16 | ⏱️ Read time: 13 min read How Physics and Machine Learning Joined Forces to Win Physics Nobel 2024

📌 Exploring DRESS Kit V2 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-16 | ⏱️ Read time: 13 min read Exploring new feature
📌 Exploring DRESS Kit V2 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-16 | ⏱️ Read time: 13 min read Exploring new features and notable changes in the latest version of the DRESS Kit

📌 A Novel Approach to Detect Coordinated Attacks Using Clustering 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-16 | ⏱️ Rea
📌 A Novel Approach to Detect Coordinated Attacks Using Clustering 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-16 | ⏱️ Read time: 18 min read Unveiling hidden patterns: grouping malicious behavior

📌 Visualization of Data with Pie Charts in Matplotlib 🗂 Category: 🕒 Date: 2024-10-16 | ⏱️ Read time: 5 min read Examples o
📌 Visualization of Data with Pie Charts in Matplotlib 🗂 Category: 🕒 Date: 2024-10-16 | ⏱️ Read time: 5 min read Examples of how to create different types of pie charts using Matplotlib to visualize the…

📌 Temporal-Difference Learning: Combining Dynamic Programming and Monte Carlo Methods for Reinforcement Learning 🗂 Category
📌 Temporal-Difference Learning: Combining Dynamic Programming and Monte Carlo Methods for Reinforcement Learning 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-17 | ⏱️ Read time: 17 min read Milestones of RL: Q-Learning and Double Q-Learning

📌 Create Your Own Prompt Enhancer from Scratch 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-17 | ⏱️ Read time: 11 min read
📌 Create Your Own Prompt Enhancer from Scratch 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-17 | ⏱️ Read time: 11 min read How to emulate OpenAI’s system prompt generator functionality

📌 Fine-Tuning BERT for Text Classification 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-17 | ⏱️ Read time: 6 min read A hacka
📌 Fine-Tuning BERT for Text Classification 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-17 | ⏱️ Read time: 6 min read A hackable example with Python code

📌 All You Need to Know to Build Radial Charts in Tableau 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-17 | ⏱️ Read time: 7 min
📌 All You Need to Know to Build Radial Charts in Tableau 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-17 | ⏱️ Read time: 7 min read You will never forget it after this!

📌 A Critical Look at AI Image Generation 🗂 Category: ART 🕒 Date: 2024-10-17 | ⏱️ Read time: 12 min read What does image ge
📌 A Critical Look at AI Image Generation 🗂 Category: ART 🕒 Date: 2024-10-17 | ⏱️ Read time: 12 min read What does image generative AI really tell us about our world?