es
Feedback
Machine Learning

Machine Learning

Ir al canal en Telegram

Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Mostrar más

📈 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
📌 LLM Fine-tuning – FAQs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-09-26 | ⏱️ Read time: 8 min read Answering the mos
📌 LLM Fine-tuning – FAQs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-09-26 | ⏱️ Read time: 8 min read Answering the most common questions I received as an AI consultant

📌 How to Make Your Data Science/ML Engineer Workflow More Effective 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read
📌 How to Make Your Data Science/ML Engineer Workflow More Effective 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 5 min read Learn how you can use VS Code interactive window to program more effectively

📌 Hyperparameter Optimization with Genetic Algorithms – A Hands-On Tutorial 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 |
📌 Hyperparameter Optimization with Genetic Algorithms – A Hands-On Tutorial 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 13 min read A step-by-step tutorial of using genetic algorithms for optimization tasks.

📌 Make Your Way from Pandas to PySpark 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 9 min read Learn a few
📌 Make Your Way from Pandas to PySpark 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 9 min read Learn a few basic commands to start transitioning from Pandas to PySpark

📌 From Zero to App: Building a Database-Driven Streamlit App with Python 🗂 Category: PROGRAMMING 🕒 Date: 2024-09-26 | ⏱️ R
📌 From Zero to App: Building a Database-Driven Streamlit App with Python 🗂 Category: PROGRAMMING 🕒 Date: 2024-09-26 | ⏱️ Read time: 6 min read A beginner’s guide to build a functional Streamlit App with SQLite Integration

Big surprise in our channels on Discord https://discord.gg/PGZku7DrSz

📌 Beyond Line and Bar Charts: 7 Less Common But Powerful Visualization Types 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 |
📌 Beyond Line and Bar Charts: 7 Less Common But Powerful Visualization Types 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 13 min read Step up your data storytelling game with these creative and insightful visualizations

📌 A Close Look at AI Pain Points, and How to (Sometimes) Resolve Them 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-
📌 A Close Look at AI Pain Points, and How to (Sometimes) Resolve Them 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 4 min read Our weekly selection of must-read Editors’ Picks and original features

📌 The Art of Tokenization: Breaking Down Text for AI 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-09-26 | ⏱️ Read time:
📌 The Art of Tokenization: Breaking Down Text for AI 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-09-26 | ⏱️ Read time: 11 min read Demystifying NLP: From Text to Embeddings

Ever tasted a wine so creamy it feels like dessert—or so crisp it dances on your tongue? Discover the secrets behind every sw
Ever tasted a wine so creamy it feels like dessert—or so crisp it dances on your tongue? Discover the secrets behind every swirl and sip with Simply Wine | Great Wine Lover. From rare finds to expert tips you won’t see elsewhere, every post uncorks the world of wine in a fresh, fascinating way. Ready to explore what’s in your glass? Join us here – your next favorite bottle is waiting! #ad InsideAds

📌 The Pareto Principle in Data Engineering 🗂 Category: ANALYTICS 🕒 Date: 2024-09-26 | ⏱️ Read time: 7 min read One step fo
📌 The Pareto Principle in Data Engineering 🗂 Category: ANALYTICS 🕒 Date: 2024-09-26 | ⏱️ Read time: 7 min read One step forward; no steps back

📌 Mastering Marketing Mix Modelling In Python 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-26 | ⏱️ Read time: 24 min read
📌 Mastering Marketing Mix Modelling In Python 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-26 | ⏱️ Read time: 24 min read Part 1 of a hands-on guide to help you master MMM in pymc

📌 A Data Scientist’s Guide to Stakeholders 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 8 min read How data
📌 A Data Scientist’s Guide to Stakeholders 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 8 min read How data scientists can best communicate with non-DS people

📌 Advice from 15 Top Data Scientists 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 8 min read Going over the
📌 Advice from 15 Top Data Scientists 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 8 min read Going over the main skills you need to be a “good” data scientist

📌 My commute to work is more than 4 hours. Each way. 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-27 | ⏱️ Read time: 11 min re
📌 My commute to work is more than 4 hours. Each way. 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-27 | ⏱️ Read time: 11 min read Am I crazy, or does the data tell a different story?

📌 How to Convert a Single HEX Color Code into a Monochrome Color Palette with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024
📌 How to Convert a Single HEX Color Code into a Monochrome Color Palette with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-27 | ⏱️ Read time: 8 min read Spoiler: It’s harder than you think.

📌 How to Perform Hyperparameter Tuning in R with Python 🗂 Category: 🕒 Date: 2024-09-27 | ⏱️ Read time: 20 min read Optimiz
📌 How to Perform Hyperparameter Tuning in R with Python 🗂 Category: 🕒 Date: 2024-09-27 | ⏱️ Read time: 20 min read Optimize your machine learning models with Reticulate & Optuna

“I just made +190 pips PROFIT in ONE HOUR—nobody believed it was possible until they saw my screen. Want in on trades the big
“I just made +190 pips PROFIT in ONE HOUR—nobody believed it was possible until they saw my screen. Want in on trades the big players don’t share? I post my exact moves daily. Miss a signal—miss your shot. See for yourself #ad InsideAds

📌 Data Science Meets Politics 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-27 | ⏱️ Read time: 8 min read Unraveling Congressio
📌 Data Science Meets Politics 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-27 | ⏱️ Read time: 8 min read Unraveling Congressional Dynamics With Networks