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

Red:Machine Learning with Python Siria225 Tecnologías y Aplicaciones3 331...

📈 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 40 334 suscriptores, ocupando la posición 3 331 en la categoría Tecnologías y Aplicaciones y el puesto 225 en la región Siria.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 334 suscriptores.

Según los últimos datos del 10 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 383, y en las últimas 24 horas de 25, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.35%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.95% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 948 visualizaciones. En el primer día suele acumular 786 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 4.
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 11 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.

40 334

Suscriptores

+2524 horas

+1227 días

+38330 días

948

Visitas de la publicación

~ 78624 horas

~ 1 06648 horas

2.35%

Tasa de compromiso

~ 2

Mensajes por día

Ads index

beta

Archivo de publicaciones

40 342

📌 Agentic AI 102: Guardrails and Agent Evaluation 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-16 | ⏱️ Read time: 12 min read An introduction to tools that make your model safer and more predictable and performant.

40 342

📌 The Automation Trap: Why Low-Code AI Models Fail When You Scale 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-16 | ⏱️ Read time: 7 min read Low-code AI platforms promise speed, a model without a single line of code. But when…

40 342

📌 How to Build an AI Journal with LlamaIndex 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-16 | ⏱️ Read time: 10 min read A step-by-step guide for building an AI assistant powered by LlamaIndex

40 342

📌 How to Set the Number of Trees in Random Forest 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-05-16 | ⏱️ Read time: 13 min read A practical introduction to the optRF package

40 342

📌 Optimizing Multi-Objective Problems with Desirability Functions 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-20 | ⏱️ Read time: 8 min read Applied to a very real problem: baking bread!

40 342

📌 I Teach Data Viz with a Bag of Rocks 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-20 | ⏱️ Read time: 5 min read Here’s Why Domain-Specific Integration Matters in Your Data Science Workflows

40 342

📌 What the Most Detailed Peer-Reviewed Study on AI in the Classroom Taught Us 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-20 | ⏱️ Read time: 8 min read A meta analysis that turns out positive yet identifies the need for further research

40 342

📌 Building AI Applications in Ruby 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-21 | ⏱️ Read time: 15 min read Why Ruby may be the best language to write your next AI web application

40 342

📌 Use PyTorch to Easily Access Your GPU 🗂 Category: PROGRAMMING 🕒 Date: 2025-05-21 | ⏱️ Read time: 12 min read Or … how an ML library can accelerate non-ML computations

40 342

📌 Top Machine Learning Jobs and How to Prepare For Them 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-05-21 | ⏱️ Read time: 8 min read Explaining the different machine learning roles

40 342

📌 About Calculating Date Ranges in DAX 🗂 Category: DATA ANALYSIS 🕒 Date: 2025-05-22 | ⏱️ Read time: 7 min read When performing date calculations, creating date ranges can be helpful. But how can we do…

40 342

📌 What Statistics Can Tell Us About NBA Coaches 🗂 Category: 🕒 Date: 2025-05-22 | ⏱️ Read time: 10 min read Using Python to determine where NBA coaches come from and what makes them successful

40 342

📌 Inheritance: A Software Engineering Concept Data Scientists Must Know To Succeed 🗂 Category: PROGRAMMING 🕒 Date: 2025-05-22 | ⏱️ Read time: 12 min read Coding concepts that distinguish an amateur from a professional data scientist

40 342

📌 Google’s AlphaEvolve: Getting Started with Evolutionary Coding Agents 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-22 | ⏱️ Read time: 20 min read Introduction AlphaEvolve 1 is a promising new coding agent by Google’s DeepMind. Let’s look at…

40 342

📌 Multiple Linear Regression Analysis 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-22 | ⏱️ Read time: 12 min read Implementation of multiple linear regression on real data: Assumption checks, model evaluation, and interpretation of…

40 342

📌 How to Evaluate LLMs and Algorithms — The Right Way 🗂 Category: THE VARIABLE 🕒 Date: 2025-05-23 | ⏱️ Read time: 3 min read This week, we focus on the best strategies for evaluating and benchmarking the performance of…

40 342

📌 Do More with NumPy Array Type Hints: Annotate & Validate Shape & Dtype 🗂 Category: PROGRAMMING 🕒 Date: 2025-05-23 | ⏱️ Read time: 5 min read Improve static analysis and run-time validation with full generic specification

40 342

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40 342

📌 Estimating Product-Level Price Elasticities Using Hierarchical Bayesian 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-05-23 | ⏱️ Read time: 21 min read Using one model to personalize ML results

40 342

📌 Prototyping Gradient Descent in Machine Learning 🗂 Category: 🕒 Date: 2025-05-23 | ⏱️ Read time: 10 min read Mathematical theorem and credit transaction prediction using Stochastic / Batch GD