¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
Artificial Intelligence
Ir al canal en Telegram
🔒 Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM
Mostrar más📈 Análisis del canal de Telegram Artificial Intelligence
El canal Artificial Intelligence (@artificial_intelligence_com) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 70 419 suscriptores, ocupando la posición 1 849 en la categoría Tecnologías y Aplicaciones y el puesto 4 785 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 70 419 suscriptores.
Según los últimos datos del 13 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 1 217, y en las últimas 24 horas de 69, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.35%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.09% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 5 179 visualizaciones. En el primer día suele acumular 1 474 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 10.
- Intereses temáticos: El contenido se centra en temas clave como learning, linkedin, linux, udemy, 040k|.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“🔒 Welcome Artificial Intelligence Channel
Buy ads: https://telega.io/c/Artificial_Intelligence_COM”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 14 junio, 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.
70 419
Suscriptores
+6924 horas
+2577 días
+1 21730 días
Archivo de publicaciones
70 430
🔅 Applied Machine Learning: Ensemble Learning
📝 Learn to use ensemble techniques like bagging, boosting, and stacking to improve your machine learning models.
🌐 Author: Matt Harrison
🔰 Level: Intermediate
⏰ Duration: 1h 28m
📋 Topics: Applied Machine Learning
🔗 Join Machine Learning for more courses
70 430
🔅 PREMIUM CHANNELS
-◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦-
🔰 Web Development
-◦-◦--◦--◦-◦--◦--◦-◦--
217k| 🔰 Linkedin Learning
143k| 🔰 Udemy Premium
132k| 🔰 Web Development
-◦-◦--◦-
121k| 🔰 Python 3
098k| 🔰 JavaScript Training
091k| 🔰 Machine Learning
-◦-◦--◦-
070k| 🔰 Data Analysis and Databases
068k| 🔰 Artificial Intelligence
064k| 🔰 Linux and DevOps
-◦-◦--◦-
063k| 🔰 React and NextJs
050k| 🔰 100 Days of Python
049k| 🔰 OpenAI Mastery
-◦-◦--◦-
049k| 🔰 Business and Finance
044k| 🔰 Best Telegram Channels
041k| 🔰 Udemy Learning
-◦-◦--◦-
040k| 🔰 Zero to Mastery
040k| 🔰 Mobile Apps
036k| 🔰 Linkedin Learning Courses
-◦-◦--◦-
035k| 🔰 Codedamn Courses
034k| 🔰 React 101
031k| 🔰 Coding Interview
-◦-◦--◦-
031k| 🔰 Crypto Tutorials
025k| 🔰 Telegram's Shorts
024k| 🔰 The Coding Space
-◦-◦--◦-
023k| 🔰 Linux Training
-◦-◦--◦--◦-◦--◦--◦-◦--
🔰 Add Your Channel
-◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦-
🔰 2hrs on top & 8hrs in channel!
70 430
🔅 Develop ML Models with Python and T-SQL
📝 Learn how to leverage Python to effectively build, train, test, and store your models in SQL Server databases.
🌐 Author: Sam Nasr
🔰 Level: Advanced
⏰ Duration: 39m
📋 Topics: Machine Learning, Microsoft SQL Server, Transact-SQL
🔗 Join Machine Learning for more courses
70 430
🔅 PREMIUM CHANNELS
-◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦-
🔰 Web Development
-◦-◦--◦--◦-◦--◦--◦-◦--
220k| 🔰 Linkedin Learning
143k| 🔰 Udemy Premium
134k| 🔰 Web Development
-◦-◦--◦-
121k| 🔰 Python 3
099k| 🔰 JavaScript Training
091k| 🔰 Machine Learning
-◦-◦--◦-
071k| 🔰 Data Analysis and Databases
069k| 🔰 Artificial Intelligence
064k| 🔰 Linux and DevOps
-◦-◦--◦-
064k| 🔰 React and NextJs
050k| 🔰 100 Days of Python
049k| 🔰 OpenAI Mastery
-◦-◦--◦-
049k| 🔰 Business and Finance
044k| 🔰 Best Telegram Channels
042k| 🔰 Udemy Learning
-◦-◦--◦-
040k| 🔰 Zero to Mastery
040k| 🔰 Mobile Apps
036k| 🔰 Linkedin Learning Courses
-◦-◦--◦-
035k| 🔰 Codedamn Courses
034k| 🔰 React 101
031k| 🔰 Crypto Tutorials
-◦-◦--◦-
031k| 🔰 Coding Interview
025k| 🔰 Telegram's Shorts
023k| 🔰 The Coding Space
-◦-◦--◦-
023k| 🔰 Linux Training
-◦-◦--◦--◦-◦--◦--◦-◦--
🔰 Add Your Channel
-◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦-
🔰 2hrs on top & 8hrs in channel!
70 430
🔅 Machine Learning Foundations: Probability
📝 Get an in-depth introduction to probability, find out why its a prerequisite for machine learning, and learn how to use it to design and implement machine learning algorithms.
🌐 Author: Terezija Semenski
🔰 Level: Beginner
⏰ Duration: 1h 24m
📋 Topics: Probability, Machine Learning
🔗 Join Machine Learning for more courses
70 430
AI Chatbots Are Making Up Fake Sources Called Grokipedia
Users and researchers have noticed that some AI chatbots sometimes generate invented source names — like “Grokipedia” — when answering questions, giving the impression of real references that don’t actually exist.
These fabricated citations aren’t reliable and can mislead people trying to verify information, especially in areas like history, science, or current events.
The issue highlights a common limitation in many generative models: they can present plausible-looking but false reference material as if it were real.
70 430
🔅 PREMIUM CHANNELS
-◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦-
🔰 Web Development
-◦-◦--◦--◦-◦--◦--◦-◦--
220k| 🔰 Linkedin Learning
143k| 🔰 Udemy Premium
134k| 🔰 Web Development
-◦-◦--◦-
121k| 🔰 Python 3
099k| 🔰 JavaScript Training
091k| 🔰 Machine Learning
-◦-◦--◦-
071k| 🔰 Data Analysis and Databases
069k| 🔰 Artificial Intelligence
064k| 🔰 Linux and DevOps
-◦-◦--◦-
064k| 🔰 React and NextJs
050k| 🔰 100 Days of Python
049k| 🔰 OpenAI Mastery
-◦-◦--◦-
049k| 🔰 Business and Finance
044k| 🔰 Best Telegram Channels
042k| 🔰 Udemy Learning
-◦-◦--◦-
040k| 🔰 Zero to Mastery
040k| 🔰 Mobile Apps
036k| 🔰 Linkedin Learning Courses
-◦-◦--◦-
035k| 🔰 Codedamn Courses
034k| 🔰 React 101
031k| 🔰 Crypto Tutorials
-◦-◦--◦-
031k| 🔰 Coding Interview
025k| 🔰 Telegram's Shorts
023k| 🔰 The Coding Space
-◦-◦--◦-
023k| 🔰 Linux Training
-◦-◦--◦--◦-◦--◦--◦-◦--
🔰 Add Your Channel
-◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦-
🔰 2hrs on top & 8hrs in channel!
70 430
🔍 Let’s decode the regression game!
Linear Regression might sound simple, but there's a whole world behind that straight line. 😉
Here are 7 powerful types of regression every data scientist should have in their toolkit:
📈 Simple Linear – One feature, one prediction line. Perfect for basic trend analysis.
📊 Multiple Linear – Multiple predictors, more accuracy. Great for real-world complexity.
🧮 Polynomial – When life (or data) isn't linear, curve it up!
🎯 Logistic – Wait... it’s for classification? Yes! Regression in name, classifier at heart.
🌀 Non-linear – Because not all relationships are straight forward.
📉 Ridge – Tackles multicollinearity with L2 regularization.
⚖️ Lasso – Feature selection king, thanks to L1 regularization.
🧠 Each model solves different data dilemmas — pick smart, experiment often!
70 430
🤝 Machine Learning Roadmap for you! 🚀
Save this post and start your journey today! 💻✨
✅ Basics of R and Python
🧮 Learn Math & Stats Concepts
🤖 Grasp ML Concepts
🦾 Master essential libraries like NumPy, Pandas, Matplotlib
⚙️Learn evaluation metrics like precision, recall, F1, and cross-validation techniques.
💪Explore deep learning, NLP, reinforcement learning, CNNs, RNNs
📊 Work on Kaggle and GitHub to tackle real-world machine learning problems
👥 Focus on Collaboration
👩💻Stay updated with courses and follow ML experts to keep learning and growing
