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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
📌 Calculating the Uncertainty Coefficient (Theil’s U) in Python 🗂 Category: PROBABILITY 🕒 Date: 2024-10-18 | ⏱️ Read time:
📌 Calculating the Uncertainty Coefficient (Theil’s U) in Python 🗂 Category: PROBABILITY 🕒 Date: 2024-10-18 | ⏱️ Read time: 5 min read A measure of correlation between discrete (categorical) variables

📌 All you need to know about Non-Inferiority Hypothesis Test 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-18 | ⏱️ Read time: 6
📌 All you need to know about Non-Inferiority Hypothesis Test 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-18 | ⏱️ Read time: 6 min read A non-inferiority test proves that a new treatment is not worse than the standard by…

📌 Implementing Anthropic’s Contextual Retrieval for Powerful RAG Performance 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-
📌 Implementing Anthropic’s Contextual Retrieval for Powerful RAG Performance 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-18 | ⏱️ Read time: 16 min read This article will show you how to implement the contextual retrieval idea proposed by Anthropic

📌 Implementing “Modular RAG” with Haystack and Hypster 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-18 | ⏱️ Read ti
📌 Implementing “Modular RAG” with Haystack and Hypster 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-18 | ⏱️ Read time: 13 min read Transforming RAG Systems into LEGO-like Reconfigurable Frameworks

📌 Cognitive Prompting in LLMs 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-19 | ⏱️ Read time: 9 min read Can we teach mach
📌 Cognitive Prompting in LLMs 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-19 | ⏱️ Read time: 9 min read Can we teach machines to think like humans?

📌 Evaluating Model Retraining Strategies 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-20 | ⏱️ Read time: 11 min read How d
📌 Evaluating Model Retraining Strategies 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-20 | ⏱️ Read time: 11 min read How data drift and concept drift matter to choose the right retraining strategy?

📌 Linked Lists – Data Structures & Algorithms for Data Scientists 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️ Read ti
📌 Linked Lists – Data Structures & Algorithms for Data Scientists 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️ Read time: 6 min read How linked lists and queues work under the hood

📌 SQL and Data Modelling in Action: A Deep Dive into Data Lakehouses 🗂 Category: SQL 🕒 Date: 2024-10-21 | ⏱️ Read time: 12
📌 SQL and Data Modelling in Action: A Deep Dive into Data Lakehouses 🗂 Category: SQL 🕒 Date: 2024-10-21 | ⏱️ Read time: 12 min read Lakehouses as a continuation of data warehouses and data lakes. What is this architecture about?

📌 Efficient Document Chunking Using LLMs: Unlocking Knowledge One Block at a Time 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Da
📌 Efficient Document Chunking Using LLMs: Unlocking Knowledge One Block at a Time 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-21 | ⏱️ Read time: 9 min read This article explains how to use an LLM (Large Language Model) to perform the chunking…

📌 The Power of Optimization in Designing Experiments Involving Small Samples 🗂 Category: 🕒 Date: 2024-10-21 | ⏱️ Read time
📌 The Power of Optimization in Designing Experiments Involving Small Samples 🗂 Category: 🕒 Date: 2024-10-21 | ⏱️ Read time: 11 min read A step-by-step guide to designing more precise experiments using optimization in Python

📌 Don’t Do Laundry Today, It Will Be Cheaper Tomorrow 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️ Read time: 19 min r
📌 Don’t Do Laundry Today, It Will Be Cheaper Tomorrow 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️ Read time: 19 min read Analysing electricity price changes in London through causal inference

📌 Awesome Plotly with Code Series (Part 1): Alternatives to Bar Charts 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️ Re
📌 Awesome Plotly with Code Series (Part 1): Alternatives to Bar Charts 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️ Read time: 14 min read A bar chart is not always the best solution.

📌 OLAP is Dead – Or Is It ? 🗂 Category: ANALYTICS 🕒 Date: 2024-10-21 | ⏱️ Read time: 16 min read OLAP’s fate in the age of
📌 OLAP is Dead – Or Is It ? 🗂 Category: ANALYTICS 🕒 Date: 2024-10-21 | ⏱️ Read time: 16 min read OLAP’s fate in the age of modern analytics

📌 Unleash the Power of Probability to Predict the Future of Your Business 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️
📌 Unleash the Power of Probability to Predict the Future of Your Business 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️ Read time: 14 min read A Practical Guide to Applying Probability Concepts with Python in Real-World Contexts

📌 Discretization, Explained: A Visual Guide with Code Examples for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-22 |
📌 Discretization, Explained: A Visual Guide with Code Examples for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 10 min read 6 fun ways to categorize numbers into bins!

📌 Using Vector Steering to Improve Model Guidance 🗂 Category: 🕒 Date: 2024-10-22 | ⏱️ Read time: 10 min read Exploring the
📌 Using Vector Steering to Improve Model Guidance 🗂 Category: 🕒 Date: 2024-10-22 | ⏱️ Read time: 10 min read Exploring the Research on Vector Steering and Coding Up an Implementation

📌 Game Theory, Part 1 – The Prisoner’s Dilemma Problem 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 7 min r
📌 Game Theory, Part 1 – The Prisoner’s Dilemma Problem 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 7 min read Game theory is prevalent in real-life scenarios and decision-making

📌 Why Scaling Works: Inductive Biases vs The Bitter Lesson 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-22 | ⏱️ Rea
📌 Why Scaling Works: Inductive Biases vs The Bitter Lesson 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 11 min read Building deep insights with a toy problem

📌 Deep Learning vs Data Science: Who Will Win? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 14 min read Wha
📌 Deep Learning vs Data Science: Who Will Win? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 14 min read What is more important, your data or your model?

📌 Self-Service ML with Relational Deep Learning 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 8 m
📌 Self-Service ML with Relational Deep Learning 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 8 min read Do ML directly on your relational database