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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 40 244 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
📌 Spoiler Alert: The Magic of RAG Does Not Come from AI 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-11-17 | ⏱️ Read time: 10
📌 Spoiler Alert: The Magic of RAG Does Not Come from AI 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-11-17 | ⏱️ Read time: 10 min read Why retrieval, not generation, makes RAG systems magical

📌 How to Reduce Python Runtime for Demanding Tasks 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-17 | ⏱️ Read time: 8 min read
📌 How to Reduce Python Runtime for Demanding Tasks 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-17 | ⏱️ Read time: 8 min read Practical techniques to accelerate heavy workloads with GPU optimization in Python

📌 From Local to Cloud: Estimating GPU Resources for Open-Source LLMs 🗂 Category: 🕒 Date: 2024-11-18 | ⏱️ Read time: 4 min
📌 From Local to Cloud: Estimating GPU Resources for Open-Source LLMs 🗂 Category: 🕒 Date: 2024-11-18 | ⏱️ Read time: 4 min read Estimating GPU memory for deploying the latest open-source LLMs

📌 Data Validation with Pandera in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-18 | ⏱️ Read time: 10 min read Validatin
📌 Data Validation with Pandera in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-18 | ⏱️ Read time: 10 min read Validating your Dataframes for Production ML Pipelines

📌 Creating a frontend for your ML application with Vercel V0 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-11-18 | ⏱️ Read tim
📌 Creating a frontend for your ML application with Vercel V0 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-11-18 | ⏱️ Read time: 9 min read Develop an appealing frontend application using v0 by Vercel

📌 Navigating Networks with NetworkX: A Short Guide to Graphs in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-18 | ⏱️ Re
📌 Navigating Networks with NetworkX: A Short Guide to Graphs in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-18 | ⏱️ Read time: 16 min read Explore NetworkX for building, analyzing, and visualizing graphs in Python. Discovering Insights in Connected Data.

📌 Increasing Transformer Model Efficiency Through Attention Layer Optimization 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date:
📌 Increasing Transformer Model Efficiency Through Attention Layer Optimization 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-11-18 | ⏱️ Read time: 16 min read How paying “better” attention can drive ML cost savings

📌 The Metrics of Continual Learning 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-11-18 | ⏱️ Read time: 4 min read Thes
📌 The Metrics of Continual Learning 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-11-18 | ⏱️ Read time: 4 min read These three metrics are commonly used

📌 Building a Local Voice Assistant with LLMs and Neural Networks on Your CPU Laptop 🗂 Category: DATA SCIENCE 🕒 Date: 2024-
📌 Building a Local Voice Assistant with LLMs and Neural Networks on Your CPU Laptop 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-19 | ⏱️ Read time: 6 min read A practical guide to run lightweight LLMs using python

📌 Dance between dense and sparse embeddings: Enabling Hybrid Search in LangChain-Milvus 🗂 Category: 🕒 Date: 2024-11-19 | ⏱
📌 Dance between dense and sparse embeddings: Enabling Hybrid Search in LangChain-Milvus 🗂 Category: 🕒 Date: 2024-11-19 | ⏱️ Read time: 7 min read Dance Between Dense and Sparse Embeddings: Enabling Hybrid Search in LangChain-Milvus How to create and…

📌 Multimodal Models – LLMs that can see and hear 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-11-19 | ⏱️ Read time: 10 min re
📌 Multimodal Models – LLMs that can see and hear 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-11-19 | ⏱️ Read time: 10 min read An introduction with example Python code

📌 The Root Cause of Why Organizations Fail With Data & AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-11-19 | ⏱️ Read
📌 The Root Cause of Why Organizations Fail With Data & AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-11-19 | ⏱️ Read time: 34 min read A guide to be successful with the strategic groundwork required

📌 NLP Illustrated, Part 1: Text Encoding 🗂 Category: DEEP LEARNING 🕒 Date: 2024-11-19 | ⏱️ Read time: 10 min read An illus
📌 NLP Illustrated, Part 1: Text Encoding 🗂 Category: DEEP LEARNING 🕒 Date: 2024-11-19 | ⏱️ Read time: 10 min read An illustrated guide to text-to-number translation, with code

📌 Linear programming: Integer Linear Programming with Branch and Bound 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-19 | ⏱️ Re
📌 Linear programming: Integer Linear Programming with Branch and Bound 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-19 | ⏱️ Read time: 11 min read Part 4: Extending linear programming optimization to discrete decision variables

📌 Third-Year Work Anniversary as a Data Scientist: Growth, Reflections and Acceptance 🗂 Category: CAREER ADVICE 🕒 Date: 20
📌 Third-Year Work Anniversary as a Data Scientist: Growth, Reflections and Acceptance 🗂 Category: CAREER ADVICE 🕒 Date: 2024-11-19 | ⏱️ Read time: 8 min read A letter to myself and fellow data scientists

📌 How to Answer Business Questions with Data 🗂 Category: BUSINESS 🕒 Date: 2024-11-19 | ⏱️ Read time: 15 min read Data anal
📌 How to Answer Business Questions with Data 🗂 Category: BUSINESS 🕒 Date: 2024-11-19 | ⏱️ Read time: 15 min read Data analysis is the key to drive business decisions through answering abstract business questions but…

📌 Collision Risk in Hash-Based Surrogate Keys 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-11-20 | ⏱️ Read time: 14 min read
📌 Collision Risk in Hash-Based Surrogate Keys 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-11-20 | ⏱️ Read time: 14 min read Various aspects and real-life analogies of the odds of having a hash collision when computing…

📌 Einstein Notation: A New Lens on Transformers 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-11-20 | ⏱️ Read time: 9 min read
📌 Einstein Notation: A New Lens on Transformers 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-11-20 | ⏱️ Read time: 9 min read Transforming the Math of the Transformer Model

📌 Water Cooler Small Talk: Why Does the Monty Hall Problem Still Bother Us? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-20 |
📌 Water Cooler Small Talk: Why Does the Monty Hall Problem Still Bother Us? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-20 | ⏱️ Read time: 10 min read A look at the counterintuitive mathematics of game show puzzles

📌 LoRA Fine-Tuning On Your Apple Silicon MacBook 🗂 Category: 🕒 Date: 2024-11-20 | ⏱️ Read time: 11 min read Let’s Go Step-
📌 LoRA Fine-Tuning On Your Apple Silicon MacBook 🗂 Category: 🕒 Date: 2024-11-20 | ⏱️ Read time: 11 min read Let’s Go Step-By-Step Fine-Tuning On Your MacBook