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 71 648 suscriptores, ocupando la posición 1 793 en la categoría Tecnologías y Aplicaciones y el puesto 4 416 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 71 648 suscriptores.
Según los últimos datos del 25 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -376, y en las últimas 24 horas de -23, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 6.25%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.58% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 4 483 visualizaciones. En el primer día suele acumular 1 129 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 7.
- 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 26 octubre, 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.
71 648
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Publicaciones del Canal
| 2 |  🔅 Responsible AI and Application Development
📝 Learn all of the steps of creating an AI application with machine learning (ML), ensuring fairness and mitigating biases throughout the process.
🌐 Author: Laurence Moroney
🔰 Level: Intermediate
⏰ Duration: 1h 37m
📋 Topics: AI Software Development, Responsible AI, Artificial Intelligence
🔗 Join Machine Learning for more courses | 1 623 |
| 3 |  +2Machine Learning vs. Deep Learning | 2 344 |
| 4 |  +3Machine Learning vs. Deep Learning | 2 117 |
| 5 |  +3Machine Learning vs. Deep Learning | 1 650 |
| 6 |  +3Machine Learning vs. Deep Learning
🤖 Machine Learning: Requires engineered features + clean data.
Examples: Credit scoring (Logistic Regression), sales forecast (Decision Trees).
🧠 Deep Learning: Takes raw data (pixels, text, audio). It learns features end-to-end via hidden layers.
Examples: Self-driving cars (CNN), voice assistants (RNN), LLMs (Transformers).
ML needs your brain. DL builds its own. | 1 689 |
| 7 | 🤖 IMPORTANT AI TERMS & TOOLS YOU SHOULD KNOW 🧠
If you want to understand modern AI better, these are some of the most important terms and tools to know.
🧠 1. Generative AI
AI that can create new content such as:
✔️ Text
✔️ Images
✔️ Audio
✔️ Video
✔️ Code
Examples include ChatGPT, Gemini and Claude.
💬 2. Large Language Model (LLM)
An AI model trained on large amounts of text to understand and generate human-like language.
LLMs power many modern AI chatbots and writing tools.
📝 3. Prompt
A prompt is the instruction or input you give to an AI system.
A clear and specific prompt generally helps you get more useful results.
🎯 4. Prompt Engineering
The practice of designing and improving prompts to get better results from AI models.
It can involve giving context, defining the task, specifying the format and providing examples.
🔢 5. Tokens
AI models process text as tokens rather than simply reading complete words.
A token can represent a word, part of a word, punctuation, or other pieces of text.
📚 6. Context Window
The amount of information an AI model can consider within a conversation or request.
A larger context window allows a model to work with more text and information at once.
🧩 7. Fine-Tuning
A process of further training an existing AI model on specific data to improve its performance for a particular task or domain.
🔎 8. RAG
RAG stands for Retrieval-Augmented Generation.
It allows an AI system to retrieve relevant information from external sources and use it when generating an answer.
This can be useful for working with company documents, knowledge bases and other specific information.
🤖 9. AI Agent
An AI agent is a system that can use AI to plan and perform multiple steps toward a goal, sometimes using external tools or applications.
For example, an agent could research information, analyze it and then prepare a report.
🔌 10. API
An API allows different software systems to communicate with each other.
AI APIs allow developers to integrate AI capabilities into websites, apps and other software.
⚡️ 11. Machine Learning
A branch of AI where systems learn patterns from data and use those patterns to make predictions or decisions.
🧠 12. Deep Learning
A type of machine learning that uses neural networks with multiple layers to learn complex patterns.
It is widely used in areas such as computer vision, speech and language processing.
🕸 13. Neural Network
A machine-learning model inspired by the way biological neural networks are structured.
Neural networks are a foundation of many modern AI systems.
👁 14. Multimodal AI
AI that can work with multiple types of information, such as:
✔️ Text
✔️ Images
✔️ Audio
✔️ Video
This allows AI systems to understand and generate different forms of content.
🛠 15. AI Tools You Should Know
Some popular categories of AI tools include:
✔️ ChatGPT — AI assistant, writing, research and problem-solving
✔️ Gemini — AI assistant and multimodal tasks
✔️ Claude — Writing, analysis and coding
✔️ Perplexity — AI-powered search and research
✔️ Midjourney — AI image generation
✔️ Runway — AI video generation and editing
✔️ GitHub Copilot — AI-assisted coding
✔️ ElevenLabs — AI voice generation | 2 344 |
| 8 |  7 Layers of LLM Stack | 2 637 |
| 9 |  4 learning paradigms in machine learning, explained visually:
1. Transfer Learning
2. Fine-tuning
3. Multi-task Learning
4. Federated Learning | 3 816 |
| 10 |  💡 Your Gateway to Exclusive Content
🔐 What is The Premium Vault?
We are a private Telegram channel dedicated to delivering high-quality, premium content that you simply cannot find through ordinary searches, free platforms, or standard telegram channels. Every piece of content inside this vault is carefully collected, researched, and created exclusively for our members.
📦 What’s Inside?
1⃣ Tutorials, and resources across various premium sites
🔢 Movies, TV Shows and Documentaries
🔢 Premium Applications, fully featured, paid-tier software and productivity tools
〰️〰️〰️〰️〰️〰️〰️〰️〰️
🚫 What You Won't Find Here:
No recycled freebies. No low-effort posts. No clickbait. Everything inside The Premium Vault is original, valuable, or rare — shared only with our inner circle of premium subscribers.
🔗 https://t.me/ThePremiumVault/4 | 3 860 |
| 11 | 📱Machine Learning
📱RAG Tuned AIs with the Cohere API Platform | 4 057 |
| 12 |  🔅 RAG Tuned AIs with the Cohere API Platform
📝 Learn how to develop RAG-based AI tuning with Cohere's API platform, optimizing models for various cloud environments.
🌐 Author: Ray Villalobos
🔰 Level: General
⏰ Duration: 21m
📋 Topics: Retrieval-Augmented Generation
🔗 Join Machine Learning for more courses | 4 066 |
| 13 |  🧠 Understanding Machine learning algorithms | 4 724 |
| 14 |  Not all Machine Learning algorithms learn the same way.
Choosing the right one can make or break your model.
This visual breaks down:
🔹 How ML algorithms differ
🔹 Supervised vs Unsupervised learning
🔹 Model complexity (simple → deep)
🔹 Popular algorithm families
🔹 Real-world business impact
📊 From Linear Models to Tree-Based Models to Neural Networks, every algorithm is a trade-off between:
✅ Accuracy
⚡️ Speed
🧩 Interpretability
📈 Real example:
The right algorithm improved customer churn prediction by +15% accuracy - turning lost users into retained customers. | 5 455 |
| 15 |  Exploring Data & AI Career Paths: Which Role Fits You?
The world of data offers diverse opportunities – here’s a breakdown of key roles in AI and data science:
1️⃣ ML Engineers
- Design and optimize data pipelines
- Work closely with data engineers
- Focus on scalable ML systems
2️⃣ ML Ops Specialists
- Manage databases & data tools
- Master storytelling & visualization
- Ensure smooth model deployment
3️⃣ Data & Analytics Experts
- Apply stats & ML modeling
- Run experiments & A/B tests
- Deliver business insights
- Specialize in metrics & reporting
Why This Matters
✅ High demand across industries
✅ Competitive salaries in tech
✅ Opportunities to work with cutting-edge AI | 4 954 |
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| 17 | 📱Machine Learning
📱How to Use AI Reasoning Models: Practical Applications with Hands-On Exercises | 5 538 |
| 18 |  🔅 How to Use AI Reasoning Models: Practical Applications with Hands-On Exercises
📝 Develop the know-how and skills to leverage OpenAI's o-Series models for advanced reasoning tasks, business applications, and scientific research.
🌐 Author: Anurag Karuparti
🔰 Level: Intermediate
⏰ Duration: 1h 59m
📋 Topics: Azure AI Studio, OpenAI API, Large Language Models
🔗 Join Machine Learning for more courses | 5 403 |
| 19 |  Normalization vs Standardization: Why They’re Not the Same
People treat these two as interchangeable. they’re not.
👉 Normalization (Min-Max scaling):
Compresses values to 0–1.
Useful when magnitude matters (pixel values, distances).
👉 Standardization (Z-score):
Centers data around mean=0, std=1.
Useful when distribution shape matters (linear/logistic regression, PCA).
🔑 Key idea:
Normalization preserves relative proportions.
Standardization preserves statistical structure.
Pick the wrong one, and your model’s geometry becomes distorted. | 6 157 |
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217k| 🔰 Linkedin Learning
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133k| 🔰 Web Development
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125k| 🔰 Learn Python 3
096k| 🔰 Learn JavaScript
095k| 🔰 Machine Learning
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071k| 🔰 Artificial Intelligence
070k| 🔰 Data Analysis and Databases
067k| 🔰 Linux and DevOps
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062k| 🔰 React and NextJs
052k| 🔰 Business and Finance
051k| 🔰 100 Days of Python
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042k| 🔰 Udemy Learning
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