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Machine Learning

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 40 205 suscriptores, ocupando la posición 3 352 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 40 205 suscriptores.

Según los últimos datos del 02 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 343, y en las últimas 24 horas de 10, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.99%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.28% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 800 visualizaciones. En el primer día suele acumular 915 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 03 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.

40 205
Suscriptores
+1024 horas
+837 días
+34330 días
Archivo de publicaciones
📌 Bayesian Linear Regression: A Complete Beginner’s guide 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-14 | ⏱️ Read time: 11 m
📌 Bayesian Linear Regression: A Complete Beginner’s guide 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-14 | ⏱️ Read time: 11 min read A workflow and code walkthrough for building a Bayesian regression model in STAN

📌 Build a Tokenizer for the Thai Language from Scratch 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-14 | ⏱️ Read ti
📌 Build a Tokenizer for the Thai Language from Scratch 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-14 | ⏱️ Read time: 16 min read A step-by-step guide to building a Thai multilingual sub-word tokenizer based on a BPE algorithm…

“I never thought I’d get access to the entire CEH v13 course… for FREE. Everyone told me, ‘That’s impossible!’ But now I’m al
“I never thought I’d get access to the entire CEH v13 course… for FREE. Everyone told me, ‘That’s impossible!’ But now I’m already learning secrets even pros don’t share. Curious what’s really inside? Check it out here before it disappears. #ad InsideAds

📌 A Powerful Feature for Boosting Python Code Efficiency and Streamlining Complex Workflows 🗂 Category: DATA SCIENCE 🕒 Dat
📌 A Powerful Feature for Boosting Python Code Efficiency and Streamlining Complex Workflows 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-15 | ⏱️ Read time: 10 min read All you need to know about Python loops

📌 Applications of Rolling Windows for Time Series, with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-15 | ⏱️ Read time:
📌 Applications of Rolling Windows for Time Series, with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-15 | ⏱️ Read time: 12 min read Here’s some powerful applications of Rolling Windows and Time Series

📌 Introducing NumPy, Part 3: Manipulating Arrays 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-15 | ⏱️ Read time: 7 min read Sh
📌 Introducing NumPy, Part 3: Manipulating Arrays 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-15 | ⏱️ Read time: 7 min read Shaping, transposing, joining, and splitting arrays

📌 OpenAI o1: Is This the Enigmatic Force That Will Reshape Every Knowledge Sector We Know? 🗂 Category: CHATGPT 🕒 Date: 202
📌 OpenAI o1: Is This the Enigmatic Force That Will Reshape Every Knowledge Sector We Know? 🗂 Category: CHATGPT 🕒 Date: 2024-09-16 | ⏱️ Read time: 7 min read My first encounters with the o1 model

📌 ASCVIT V1: Automatic Statistical Calculation, Visualization and Interpretation Tool 🗂 Category: DATA SCIENCE 🕒 Date: 202
📌 ASCVIT V1: Automatic Statistical Calculation, Visualization and Interpretation Tool 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 38 min read Automated data analysis made easy: The first version of ASCVIT the tool for statistical calculation,…

📌 What Makes a Great Data Business 🗂 Category: BUSINESS 🕒 Date: 2024-09-16 | ⏱️ Read time: 8 min read Including an easy-to
📌 What Makes a Great Data Business 🗂 Category: BUSINESS 🕒 Date: 2024-09-16 | ⏱️ Read time: 8 min read Including an easy-to-use data business evaluation cheat sheet

📌 Temporal-Difference Learning and the Importance of Exploration: An Illustrated Guide 🗂 Category: ARTIFICIAL INTELLIGENCE
📌 Temporal-Difference Learning and the Importance of Exploration: An Illustrated Guide 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-10-01 | ⏱️ Read time: 18 min read Comparing model-free and model-based RL methods on a dynamic grid world

📌 GPU Accelerated Polars – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-17 |
📌 GPU Accelerated Polars – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-17 | ⏱️ Read time: 16 min read Fast Dataframes for Big Problems

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📌 Building RAGs Without A Retrieval Model Is a Terrible Mistake 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-17 | ⏱️ Read time
📌 Building RAGs Without A Retrieval Model Is a Terrible Mistake 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-17 | ⏱️ Read time: 10 min read Here are my favorite techniques – one is faster, the other is more accurate.

📌 The Mystery Behind the PyTorch Automatic Mixed Precision Library 🗂 Category: DEEP LEARNING 🕒 Date: 2024-09-17 | ⏱️ Read
📌 The Mystery Behind the PyTorch Automatic Mixed Precision Library 🗂 Category: DEEP LEARNING 🕒 Date: 2024-09-17 | ⏱️ Read time: 9 min read How to get 2X speed up model training using three lines of code

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Ever wonder how real traders grow $1,000 into proven profits—step by step, with full transparency? Elite Gold Trading opens the door to professional copytrading, verified results, and exclusive strategies you can follow today. New members get a 100% deposit bonus—start with a real edge from day one. Ready to see how the pros do it? Join now & claim your bonus before this offer ends! #ad InsideAds

📌 Introduction to Maximum Likelihood Estimates 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-18 | ⏱️ Read time: 9 min read Lear
📌 Introduction to Maximum Likelihood Estimates 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-18 | ⏱️ Read time: 9 min read Learn about Maximum Likelihood Estimates via their application for next-word prediction

📌 Unlocking Business Potential Through Effective Customer Segmentation 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-18 | ⏱️ Re
📌 Unlocking Business Potential Through Effective Customer Segmentation 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-18 | ⏱️ Read time: 9 min read Transform your data into actionable insights with customer segmentation for improved engagement and profitability

📌 Asking for Feedback as a Data Scientist Individual Contributor 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-18 | ⏱️ Read tim
📌 Asking for Feedback as a Data Scientist Individual Contributor 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-18 | ⏱️ Read time: 17 min read Receive clear and useful feedback. Ditch generic questions. More than 60 example questions for you…

📌 Under the Hood: How DAX Works with Filters 🗂 Category: POWER BI 🕒 Date: 2025-10-01 | ⏱️ Read time: 6 min read Have you e
📌 Under the Hood: How DAX Works with Filters 🗂 Category: POWER BI 🕒 Date: 2025-10-01 | ⏱️ Read time: 6 min read Have you ever wondered how DAX works with filters in Measures? Today, I take a…

📌 Visual Pollen Classification Using CNNs and Vision Transformers 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-10-01 | ⏱️ Rea
📌 Visual Pollen Classification Using CNNs and Vision Transformers 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-10-01 | ⏱️ Read time: 19 min read Filling the data gap: A machine learning approach to pollen identification in ecology and biotechnology