Artificial Intelligence
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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 72 399 suscriptores, ocupando la posición 1 724 en la categoría Tecnologías y Aplicaciones y el puesto 4 344 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 72 399 suscriptores.
Según los últimos datos del 31 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 363, y en las últimas 24 horas de -19, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 6.53%. 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 4 727 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 13.
- 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 01 septiembre, 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.
72 399
Suscriptores
-1924 horas
-1267 días
+36330 días
Archivo de publicaciones
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📱Artificial Intelligence and Machine Learning
📱High-Performance PySpark: Advanced Strategies for Optimal Data Processing
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📂 Full description
Master the art of efficient data processing with this advanced PySpark course designed for data engineers. Instructor Ameena Ansari shows you the essentials of optimizing the data cleaning process and defining schemas to streamline ingestion at scale. Explore various data formats and compression techniques to ensure seamless performance, even with massive datasets. By the end of this course, you'll have the tools and skills you need to transform and ingest high-quality data using PySpark pipelines that are both scalable and efficient.This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that youll likely encounter in the workplace. Check out “Using GitHub Codespaces" with this course to learn how to get started.
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🔅 High-Performance PySpark: Advanced Strategies for Optimal Data Processing
🌐 Author: Ameena Ansari
🔰 Level: Advanced
⏰ Duration: 1h 22m
🌀 Discover techniques for optimizing data cleaning, selecting efficient data formats, minimizing shuffling and skew, and performing high-performance data processing at scale.📗 Topics: Data Pipelines, PySpark, Data Processing 📤 Join Artificial Intelligence and Machine Learning for more courses
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🔗 30 Useful AI Apps That Can Help You in 2025
AI apps are taking over the world. There’s an AI app for every conceivable use case. Here are some AI apps for different categories:1 - General Purpose: Perplexity, Anthropic Claude, Grok, ChatGPT, and Gemini 2 - Writing Code: Cursor, Replit, Windsurf AI, Github Copilot, and Tabnine 3 - Productivity: Adobe (PDF Chat), Gemini for Gmail, Gamma (AI slide deck), WisprFlow (AI voice dictation), and Granola (AI notetaker) 4 - Audience Building: Delphi (AI text, voice), HeyGen (video translation), Persona (AI agent builder), Captions (AI video editing), and OpusClips (Video repurposing) 5 - Creativity: ElevenLabs (realistic AI voices), Midjourney, Suno AI (music generation), Krea (enhance images), and Photoroom (AI image editing) 6 - Learning and Growth: Particle News App, Rosebud (AI journal app), NotebookLM, GoodInside (parenting co-pilot), and Ash (AI counselor).
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📱Artificial Intelligence and Machine Learning
📱Artificial Intelligence Foundations: Machine Learning
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📂 Full description
Machine learning is the most exciting branch of artificial intelligence. It allows systems to learn from data by identifying patterns and making decisions with little to no human intervention. In this course, you'll navigate the machine learning lifecycle by getting hands-on practice training your first machine learning model. Join instructor Kesha Williams as she explores widely adopted machine learning methods: supervised, unsupervised, and reinforcement. There's a focus on sourcing and preparing data and selecting the best learning algorithm for your project. After training a model, learn to evaluate model performance using standard metrics. Finally, Kesha shows you how to streamline the process by building a machine learning pipeline. If youre looking to understand the machine learning lifecycle and the steps required to build systems, check out this course.
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🔅 Artificial Intelligence Foundations: Machine Learning
🌐 Author: Kesha Williams
🔰 Level: Beginner
⏰ Duration: 1h 50m
🌀 Learn about the machine learning lifecycle and the steps required to build systems in this hands-on course.📗 Topics: Machine Learning, Artificial Intelligence 📤 Join Artificial Intelligence and Machine Learning for more courses
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8. Set up the user interface and trigger the main function.
• Provides an input field for the user's question
• Triggers the main function when the user clicks "Get Answer"
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7. Define the main function to run all LLMs and aggregate results.
• Runs all reference models asynchronously
• Displays individual responses in expandable sections
• Aggregates responses using the aggregator model
• Streams the aggregated response.
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6. Implement the LLM call function.
• Asynchronously calls the LLM with the user's prompt
• Returns the model name and its response
