Artificial Intelligence
Ir al canal en Telegram
🔒 Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM
Mostrar más
Red:📈 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 72 419 suscriptores, ocupando la posición 1 719 en la categoría Tecnologías y Aplicaciones y el puesto 4 348 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 72 419 suscriptores.
Según los últimos datos del 30 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 405, y en las últimas 24 horas de -37, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 6.63%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.94% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 4 801 visualizaciones. En el primer día suele acumular 1 407 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 11.
- 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 31 agosto, 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.
72 419
Suscriptores
-3724 horas
-1227 días
+40530 días
Archivo de publicaciones
72 410
📱Artificial Intelligence and Machine Learning
📱Machine Learning and AI Foundations: Clustering and Association
72 410
🔅 Machine Learning and AI Foundations: Clustering and Association
📝 Learn how to use cluster analysis, association rules, and anomaly detection algorithms for unsupervised learning.
🌐 Author: Keith McCormick
🔰 Level: Intermediate
⏰ Duration: 3h 33m
📋 Topics: Machine Learning, Artificial Intelligence
🔗 Join Artificial Intelligence and Machine Learning for more courses
72 410
🔅 PREMIUM CHANNELS
-◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦-
🔰 Web Development
-◦-◦--◦--◦-◦--◦--◦-◦--
221k| 🔰 Linkedin Learning
139k| 🔰 Udemy Premium
134k| 🔰 Web Development
-◦-◦--◦-
118k| 🔰 Python 3
100k| 🔰 JavaScript Training
089k| 🔰 Machine Learning
-◦-◦--◦-
068k| 🔰 Artificial Intelligence
068k| 🔰 Data Analysis and Databases
064k| 🔰 React and NextJs
-◦-◦--◦-
061k| 🔰 Linux and DevOps
049k| 🔰 100 Days of Python
048k| 🔰 OpenAI Mastery
-◦-◦--◦-
047k| 🔰 Business and Finance
045k| 🔰 Best Telegram Channels
040k| 🔰 Udemy Learning
-◦-◦--◦-
040k| 🔰 Zero to Mastery
040k| 🔰 Mobile Apps
035k| 🔰 Linkedin Learning Courses
-◦-◦--◦-
035k| 🔰 Codedamn Courses
034k| 🔰 React 101
031k| 🔰 Crypto Tutorials
-◦-◦--◦-
030k| 🔰 Coding Interview
025k| 🔰 Telegram's Shorts
022k| 🔰 Linux Training
-◦-◦--◦-
022k| 🔰 The Coding Space
-◦-◦--◦--◦-◦--◦--◦-◦--
🔰 Add Your Channel
-◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦-
🔰 2hrs on top & 8hrs in channel!
72 410
📱Artificial Intelligence and Machine Learning
📱Choosing the Right ML Approach for Your Business Case
72 410
🔅 Choosing the Right ML Approach for Your Business Case
📝 Learn the system components of machine learning (ML), their function in the AI ecosystem, and how to choose the best approach for your business pipeline.
🌐 Author: Lyron Andrews
🔰 Level: Intermediate
⏰ Duration: 1h 42m
📋 Topics: Machine Learning, Artificial Intelligence
🔗 Join Artificial Intelligence and Machine Learning for more courses
72 410
🔅 PREMIUM CHANNELS
-◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦-
🔰 Web Development
-◦-◦--◦--◦-◦--◦--◦-◦--
221k| 🔰 Linkedin Learning
138k| 🔰 Udemy Premium
133k| 🔰 Web Development
-◦-◦--◦-
117k| 🔰 Python 3
100k| 🔰 JavaScript Training
088k| 🔰 Machine Learning
-◦-◦--◦-
067k| 🔰 Artificial Intelligence
067k| 🔰 Data Analysis and Databases
064k| 🔰 React and NextJs
-◦-◦--◦-
061k| 🔰 Linux and DevOps
049k| 🔰 100 Days of Python
047k| 🔰 OpenAI Mastery
-◦-◦--◦-
047k| 🔰 Business and Finance
044k| 🔰 Best Telegram Channels
040k| 🔰 Udemy Learning
-◦-◦--◦-
040k| 🔰 Zero to Mastery
040k| 🔰 Mobile Apps
035k| 🔰 Linkedin Learning Courses
-◦-◦--◦-
035k| 🔰 Codedamn Courses
034k| 🔰 React 101
031k| 🔰 Crypto Tutorials
-◦-◦--◦-
030k| 🔰 Coding Interview
025k| 🔰 Telegram's Shorts
022k| 🔰 Linux Training
-◦-◦--◦-
022k| 🔰 The Coding Space
-◦-◦--◦--◦-◦--◦--◦-◦--
🔰 Add Your Channel
-◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦-
🔰 2hrs on top & 8hrs in channel!
72 410
🔗 Machine Learning, Simplified
Ever wondered what Machine Learning really means and how it impacts your everyday life?
ML is not just about fancy algorithms—it's about how machines learn like humans to make decisions, automate tasks, and personalize your digital experiences. 🔍✨
Here are real-world use cases you interact with daily:
🔹 Generative AI → ChatGPT, Midjourney
🔹 Speech Recognition → Siri, Alexa
🔹 Computer Vision → Face ID, Self-driving cars
🔹 RPA & Stock Trading Bots → Automating workflows & finance!
72 410
🔅 GraphRAG Essential Training
📝 Learn how to build robust AI applications by creating knowledge graphs for retrieval-augmented generation (RAG) in Python using LangChain and Neo4j.
🌐 Author: Dr. Clair Sullivan
🔰 Level: Intermediate
⏰ Duration: 1h 39m
📋 Topics: Retrieval-Augmented Generation, Knowledge Graph Augmentation, Knowledge Graphs
🔗 Join Artificial Intelligence and Machine Learning for more courses
72 410
🔰 Why Python is a Must-Have Skill?
If you're diving into programming or data science, mastering Python is essential! Its versatility and simplicity make it the go-to language across industries.◆ Powerful and Versatile From web development to data analysis, Python’s broad libraries and frameworks adapt to almost any project. ◆ Data-Driven Python, combined with libraries like Pandas and NumPy, allows you to analyze and manipulate datasets efficiently. ◆ Automate the Boring Stuff Automate repetitive tasks, streamline workflows, and boost productivity with Python’s easy-to-use scripts. ◆ AI and Machine Learning With frameworks like TensorFlow and Scikit-learn, Python is at the forefront of AI, enabling you to build predictive models and explore deep learning. ◆ Readable and Beginner-Friendly Python’s simple syntax makes it easy to learn, even for beginners, without sacrificing power and functionality. ◆ Community Support Backed by a massive global community, Python is constantly evolving, with new libraries and resources available at your fingertips.
72 410
+1
⚡️ Agentic Reward Modeling is a fresh project from THU-KEG, the goal of which is to rethink the approach to training agent systems.
This tool aims to develop reward methods where the agent does not simply follow commands, but learns to understand its actions in the context of more complex tasks and long-term goals.
Key Features:
- Instead of standard RL methods, where rewards often depend on pre-set criteria, the emphasis here is on developing more complex strategies that adapt to changing environments and goals.
- The tool helps model rewards in such a way that the agent can independently adjust its actions, learn from mistakes and, ultimately, demonstrate more “human” decision making.
- Developers can use this approach in multi-agent systems and complex tasks where dynamic assessment of the effectiveness of actions is important.
This tool is interesting not only for its theoretical potential, but also for its practical applications in the field of creating more autonomous and intelligent systems. Agentic Reward Modeling opens up new possibilities for studying agents that can learn in real time, which makes it promising for further research and integration into real applications.
▪️Paper: https://arxiv.org/abs/2502.19328
▪️Code: https://github.com/THU-KEG/Agentic-Reward-Modeling
