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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 40 191 suscriptores, ocupando la posición 3 381 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 40 191 suscriptores.

Según los últimos datos del 01 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 355, y en las últimas 24 horas de 21, 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 2.12% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 818 visualizaciones. En el primer día suele acumular 851 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 02 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.

40 191
Suscriptores
+2124 horas
+857 días
+35530 días
Archivo de publicaciones
📌 Visualising Strava Race Analysis 🗂 Category: 🕒 Date: 2024-08-06 | ⏱️ Read time: 17 min read Two New Graphs That Compare
📌 Visualising Strava Race Analysis 🗂 Category: 🕒 Date: 2024-08-06 | ⏱️ Read time: 17 min read Two New Graphs That Compare Runners on the Same Event

📌 Create Synthetic Dataset Using Llama 3.1 to Fine-Tune Your LLM 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-07 | ⏱️ Read tim
📌 Create Synthetic Dataset Using Llama 3.1 to Fine-Tune Your LLM 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-07 | ⏱️ Read time: 10 min read Using the giant Llama 3.1 405B and Nvidia Nemotron 4 reward model to create a…

📌 Stop Wasting LLM Tokens 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-07 | ⏱️ Read time: 5 min read Batching your inputs toge
📌 Stop Wasting LLM Tokens 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-07 | ⏱️ Read time: 5 min read Batching your inputs together can lead to substantial savings without compromising on performance

📌 Strategizing Your Preparation for Machine Learning Interviews 🗂 Category: CAREER ADVICE 🕒 Date: 2024-08-07 | ⏱️ Read tim
📌 Strategizing Your Preparation for Machine Learning Interviews 🗂 Category: CAREER ADVICE 🕒 Date: 2024-08-07 | ⏱️ Read time: 10 min read Decoding Job Roles and identify focus areas

📌 High-Performance Data Processing: pandas 2 vs. Polars, a vCPU Perspective 🗂 Category: 🕒 Date: 2024-08-07 | ⏱️ Read time:
📌 High-Performance Data Processing: pandas 2 vs. Polars, a vCPU Perspective 🗂 Category: 🕒 Date: 2024-08-07 | ⏱️ Read time: 8 min read Polars promises its multithreading capabilities outperform pandas. But is it also the case with a…

📌 Short and Sweet: Enhancing LLM Performance with Constrained Chain-of-Thought 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date:
📌 Short and Sweet: Enhancing LLM Performance with Constrained Chain-of-Thought 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-07 | ⏱️ Read time: 10 min read Sometimes few words are enough: reducing output length for increasing accuracy

📌 AI Shapeshifters: The Changing Role of the AI Engineer and Applied Data Scientist 🗂 Category: DATA SCIENCE 🕒 Date: 2024-
📌 AI Shapeshifters: The Changing Role of the AI Engineer and Applied Data Scientist 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-07 | ⏱️ Read time: 5 min read The role of AI Engineer and Applied Data Scientist has undergone a remarkable transformation. Where…

📌 Reinforcement Learning, Part 6: n-step Bootstrapping 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-07 | ⏱️ Read ti
📌 Reinforcement Learning, Part 6: n-step Bootstrapping 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-07 | ⏱️ Read time: 7 min read Pushing the boundaries: generalizing temporal difference algorithms

📌 Spatial Interpolation in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 4 min read Using the Inverse
📌 Spatial Interpolation in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 4 min read Using the Inverse Distance Weighting method to infer missing spatial data

📌 How to Use Machine Learning to Inform Design Decisions and Make Predictions 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-08
📌 How to Use Machine Learning to Inform Design Decisions and Make Predictions 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 15 min read An Introductory Guide and Use Case for Applied Data Science

📌 5 Proven Query Translation Techniques To Boost Your RAG Performance 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-
📌 5 Proven Query Translation Techniques To Boost Your RAG Performance 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 11 min read How to get near-perfect LLM performance even with ambiguous user inputs

📌 The Big Questions Shaping AI Today 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 4 min read Our
📌 The Big Questions Shaping AI Today 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 4 min read Our weekly selection of must-read Editors’ Picks and original features

📌 3 Key Tweaks That Will Make Your Matplotlib Charts Publication Ready 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-08 | ⏱️ Re
📌 3 Key Tweaks That Will Make Your Matplotlib Charts Publication Ready 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 4 min read Matplotlib charts are an eyesore by default – here’s what to do about it.

📌 Ask Not What AI Can Do for You – Ask What You Can Achieve with AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-08
📌 Ask Not What AI Can Do for You – Ask What You Can Achieve with AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-08 | ⏱️ Read time: 11 min read Unlock AI for Everyone: Discover How You Can Use LLMs in Everyday Tasks

📌 Create Stronger Decision Trees with bootstrapping and genetic algorithms 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 202
📌 Create Stronger Decision Trees with bootstrapping and genetic algorithms 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 31 min read A technique to better allow decision trees to be used as interpretable models

📌 We Need to Raise the Bar for AI Product Managers 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time:
📌 We Need to Raise the Bar for AI Product Managers 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 10 min read How to Stop Blaming the ‘Model’ and Start Building Successful AI Products

📌 LLMOps – Serve a Llama-3 model with BentoML 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 5 min
📌 LLMOps – Serve a Llama-3 model with BentoML 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 5 min read Quickly set up LLM APIs with BentoML and Runpod

📌 AI for the Absolute Novice – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-
📌 AI for the Absolute Novice – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 40 min read From “I’ve never coded” to making an AI model from scratch.

📌 KernelSHAP can be misleading with correlated predictors 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read
📌 KernelSHAP can be misleading with correlated predictors 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 7 min read A concrete case study

📌 Pre-Commit & Git Hooks: Automate High Code Quality 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time
📌 Pre-Commit & Git Hooks: Automate High Code Quality 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-09 | ⏱️ Read time: 6 min read How to improve your code quality with pre-commit and git hooks