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 71 673 suscriptores, ocupando la posición 1 773 en la categoría Tecnologías y Aplicaciones y el puesto 4 477 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 71 673 suscriptores.
Según los últimos datos del 16 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 1 172, y en las últimas 24 horas de 17, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 10.53%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.39% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 7 550 visualizaciones. En el primer día suele acumular 1 716 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 17.
- 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 17 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.
71 673
Suscriptores
+1724 horas
+4207 días
+1 17230 días
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| Fecha | Crecimiento de Suscriptores | Menciones | Canales | |
| 17 julio | +5 | |||
| 16 julio | +17 | |||
| 15 julio | +30 | |||
| 14 julio | +43 | |||
| 13 julio | +32 | |||
| 12 julio | +33 | |||
| 11 julio | +23 | |||
| 10 julio | +247 | |||
| 09 julio | +132 | |||
| 08 julio | +29 | |||
| 07 julio | +42 | |||
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| 05 julio | +36 | |||
| 04 julio | +29 | |||
| 03 julio | +31 | |||
| 02 julio | +37 | |||
| 01 julio | +40 |
Publicaciones del Canal
If you already have 200 open tabs with courses, articles, and GitHub repositories on ML, this repository might save the situation a bit. 😅
Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. 🤖
Instead of endless Google searches, everything is organized into categories:
• fundamentals of machine learning
• neural networks and modern architectures
• tasks and application areas
• datasets
• libraries and tools
• fairness and AI ethics
• production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. 📝
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. ⚠️
🌐 https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
| 2 | ✅ Top Artificial Intelligence Concepts You Should Know 🤖🧠
🔹 1. Natural Language Processing (NLP)
Use Case: Chatbots, language translation
→ Enables machines to understand and generate human language.
🔹 2. Computer Vision
Use Case: Face recognition, self-driving cars
→ Allows machines to "see" and interpret visual data.
🔹 3. Machine Learning (ML)
Use Case: Predictive analytics, spam filtering
→ AI learns patterns from data to make decisions without explicit programming.
🔹 4. Deep Learning
Use Case: Voice assistants, image recognition
→ A type of ML using neural networks with many layers for complex tasks.
🔹 5. Reinforcement Learning
Use Case: Game AI, robotics
→ AI learns by interacting with the environment and receiving feedback.
🔹 6. Generative AI
Use Case: Text, image, and music generation
→ Models like ChatGPT or DALL·E create human-like content.
🔹 7. Expert Systems
Use Case: Medical diagnosis, legal advice
→ AI systems that mimic decision-making of human experts.
🔹 8. Speech Recognition
Use Case: Voice search, virtual assistants
→ Converts spoken language into text.
🔹 9. AI Ethics
Use Case: Bias detection, fair AI systems
→ Ensures responsible and transparent AI usage.
🔹 10. Robotic Process Automation (RPA)
Use Case: Automating repetitive office tasks
→ Uses AI to handle rule-based digital tasks efficiently.
💡 Learn these concepts to understand how AI is transforming industries!
💬 Tap ❤️ for more! | 2 030 |
| 3 |  🔰 Learn Python and Machine Learning | 3 716 |
| 4 | 📦 Exercise Files | 5 000 |
| 5 | 📱Machine Learning
📱The AI Ecosystem for Developers: Models, Datasets, and APIs | 4 901 |
| 6 |  🔅 The AI Ecosystem for Developers: Models, Datasets, and APIs
📝 This is a comprehensive guide to understanding key components of the AI ecosystem: models, datasets, and APIs.
🌐 Author: Wuraola Oyewusi
🔰 Level: Intermediate
⏰ Duration: 3h 31m
📋 Topics: AI Software Development, Large Language Models, Generative AI
🔗 Join Machine Learning for more courses | 4 719 |
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🔰 2hrs on top & 8hrs in channel! | 1 873 |
| 8 |  🤝 Types of Machine Learning | 5 995 |
| 9 |  Most AI engineers never fully understood the maths behind what they build! 🤯🧮
This is an open, unconventional textbook covering maths, CS, and AI from the ground up, written for curious practitioners who want to deeply understand the field, not just survive an interview. 📘✨
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- Vectors, linear algebra, calculus, and optimization 📐📉
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- Transformer architectures and LLMs 🦄
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- AI inference and deployment 🌐
Ships with an MCP server so Claude Code, Cursor, and any MCP-compatible agent can use the compendium as a live knowledge base during development. You only need elementary maths and basic Python to start. 🐍🏗
🌐 Repo: https://github.com/HenryNdubuaku/maths-cs-ai-compendium | 7 511 |
| 10 | The only LLM cheat sheet you'll ever need 🚀
Covers the main concepts, architectures, and practical applications.
Basics
- Tokens (tokenization, BPE)
- Embeddings (cosine similarity)
- Attention mechanism (Attention formula, Multi-Head Attention)
Transformer architecture and its variants
- BERT (models with only an encoder)
- GPT (models with only a decoder)
- T5 (models with an encoder and a decoder)
Large language models (LLMs)
- Prompting (context length, Chain-of-Thought)
- Pre-training (SFT, PEFT/LoRA)
- Preference tuning (Reward Model, Reinforcement Learning)
- Optimizations (Mixture of Experts, Distillation, Quantization)
Applications
- LLM-as-a-Judge (LaaJ)
- RAG (Retrieval-Augmented Generation)
- Agents (ReAct)
- Reasoning models (Scaling) | 7 198 |
| 11 | 📱Machine Learning
📱AI Sentiment Analysis with PyTorch and Hugging Face Transformers | 8 710 |
| 12 |  🔅 AI Sentiment Analysis with PyTorch and Hugging Face Transformers
📝 Build and deploy a sentiment analysis model using Hugging Face Transformers and PyTorch.
🌐 Author: Zhongyu Pan
🔰 Level: Beginner
⏰ Duration: 32m
📋 Topics: PyTorch, Sentiment Analysis
🔗 Join Machine Learning for more courses | 8 192 |
| 13 |  👍 Top 6 Types of AI Models | 8 423 |
| 14 |  🚀 8 Types of AI Agents You Should Know
AI agents are evolving beyond just text generation. Different architectures are being designed to specialize in reasoning, perception, action, and abstraction. Here’s a quick breakdown:
1️⃣ GPTs – general-purpose text generators, great for fluency and versatility.
2️⃣ MoE (Mixture of Experts) – route tasks to specialized subnetworks for efficiency.
3️⃣ Large Reasoning Models – optimized for multi-step logical reasoning.
4️⃣ Vision-Language Models – bridge perception and language for multimodal tasks.
5️⃣ Small Language Models – lightweight, cost-efficient agents for edge deployment.
6️⃣ Large Action Models – built to execute code, call APIs, and perform tasks autonomously.
7️⃣ Hierarchical Language Models – break problems into sub-tasks, enabling long-horizon planning.
8️⃣ Large Concept Models – capture abstract, high-level knowledge for generalization.
🔍 What this really shows is that “AI agents” are no longer a monolithic idea. They’re evolving into a system of complementary architectures—each optimized for a different layer of intelligence. | 8 724 |
| 15 |  📱 Understanding Machine learning algorithms | 7 924 |
| 16 | 📦 Exercise Files | 8 427 |
| 17 | 📱Machine Learning
📱Natural Language Processing with PyTorch | 8 663 |
| 18 |  🔅 Natural Language Processing with PyTorch
📝 Learn the basics of using PyTorch, a powerful deep learning tool, for natural language processing.
🌐 Author: Zhongyu Pan
🔰 Level: Intermediate
⏰ Duration: 41m
📋 Topics: Natural Language Processing, PyTorch
🔗 Join Machine Learning for more courses | 8 634 |
| 19 |  👑 Types of Machine Learning | 7 973 |
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