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 72 523 suscriptores, ocupando la posición 1 740 en la categoría Tecnologías y Aplicaciones y el puesto 4 444 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 72 523 suscriptores.
Según los últimos datos del 25 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 588, y en las últimas 24 horas de -6, 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 1.91% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 4 806 visualizaciones. En el primer día suele acumular 1 386 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 14.
- 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 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.
72 523
Suscriptores
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+847 días
+58830 días
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Publicaciones del Canal
+4
📊 Scikit-learn is your go-to Python library for building machine learning models — fast, flexible, and beginner-friendly! Whether you're tackling classification, regression, clustering, or dimensionality reduction, it has all the tools you need.
💡 Built on NumPy, SciPy, and matplotlib, it makes tasks like model training, cross-validation, and evaluation super smooth.
| 2 |  +3Scikit-learn | 991 |
| 3 |  🤝 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 | 2 620 |
| 4 |  +3🤝 Confusion matrix | 3 388 |
| 5 | 📱Machine Learning
📱Execute and Evaluate Hugging Face AI Models | 4 042 |
| 6 |  🔅 Execute and Evaluate Hugging Face AI Models
📝 Discover essential, in-demand skills for leveraging pretrained models. Learn how to select, implement, and evaluate models using the machine learning platform Hugging Face.
🌐 Author: Kendall Ruber
🔰 Level: Intermediate
⏰ Duration: 1h 18m
📋 Topics: Generative AI, Machine Learning, Artificial Intelligence
🔗 Join Machine Learning for more courses | 3 834 |
| 7 |  💡 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 | 3 573 |
| 8 |  +5Machine Learning cheat sheet | 4 867 |
| 9 |  +9Top 10 Python Libraries for AI & ML | 5 275 |
| 10 |  🔍 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! | 5 139 |
| 11 | 📱Machine Learning
📱A Hands-On Introduction to Hugging Face for Developers | 5 766 |
| 12 |  🔅 A Hands-On Introduction to Hugging Face for Developers
📝 Learn to navigate the Hugging Face ecosystem, fine-tune pre-trained models, and build a conversational AI using Llama 3.
🌐 Author: Dhhyey Desai
🔰 Level: Intermediate
⏰ Duration: 44m
📋 Topics: Hugging Face Products, Data Science, Machine Learning
🔗 Join Machine Learning for more courses | 5 721 |
| 13 |  +5n8n cheat sheet
I wish I had this cheat sheet when I started automating using n8n.
Save this before it disappears. This cheat sheet covers everything from triggers to AI agents, expressions to keyboard shortcuts. Whether you're building your first workflow or your hundredth, you'll want this in your back pocket. | 5 886 |
| 14 | Creating a data science and machine learning project involves several steps, from defining the problem to deploying the model. Here is a general outline of how you can create a data science and ML project:
1. Define the Problem: Start by clearly defining the problem you want to solve. Understand the business context, the goals of the project, and what insights or predictions you aim to derive from the data.
2. Collect Data: Gather relevant data that will help you address the problem. This could involve collecting data from various sources, such as databases, APIs, CSV files, or web scraping.
3. Data Preprocessing: Clean and preprocess the data to make it suitable for analysis and modeling. This may involve handling missing values, encoding categorical variables, scaling features, and other data cleaning tasks.
4. Exploratory Data Analysis (EDA): Perform exploratory data analysis to understand the data better. Visualize the data, identify patterns, correlations, and outliers that may impact your analysis.
5. Feature Engineering: Create new features or transform existing features to improve the performance of your machine learning model. Feature engineering is crucial for building a successful ML model.
6. Model Selection: Choose the appropriate machine learning algorithm based on the problem you are trying to solve (classification, regression, clustering, etc.). Experiment with different models and hyperparameters to find the best-performing one.
7. Model Training: Split your data into training and testing sets and train your machine learning model on the training data. Evaluate the model's performance on the testing data using appropriate metrics.
8. Model Evaluation: Evaluate the performance of your model using metrics like accuracy, precision, recall, F1-score, ROC-AUC, etc. Make sure to analyze the results and iterate on your model if needed.
9. Deployment: Once you have a satisfactory model, deploy it into production. This could involve creating an API for real-time predictions, integrating it into a web application, or any other method of making your model accessible.
10. Monitoring and Maintenance: Monitor the performance of your deployed model and ensure that it continues to perform well over time. Update the model as needed based on new data or changes in the problem domain. | 6 009 |
| 15 | 🏠🤖 Run Your Own LOCAL LLM (Beginner Friendly)
LLMs are cool, but running your own local one hits different 😎
No cloud. No API keys. No limits.
🧩 Step 1: Install Ollama
Install Ollama on your machine (works on Mac, Windows, Linux).
Once installed, open your terminal.
🚀 Step 2: Run a model
ollama run llama3.2
This command:
• Downloads the model
• Starts it locally
• Lets you chat instantly 💬
If you see the prompt, your local LLM is running.
⚙️ Step 3: Do local inference (API style)
Ollama runs a local server on your machine.
curl http://127.0.0.1:11434/api/generate \
-H "Content-Type: application/json" \
-d '{
"model": "llama3.2",
"prompt": "Explain overfitting like I am 12",
"stream": false
}'
If you get a JSON response with text → ✅ it works.
💡 Why this is powerful
• Works offline
• Private by default
• Perfect for learning, testing, and small apps
This is the easiest way to start with LLMs locally. | 6 385 |
| 16 | 📱Machine Learning
📱Machine Learning in Mobile Applications | 6 254 |
| 17 |  🔅 Machine Learning in Mobile Applications
📝 Explore scenarios for using machine learning within mobile development.
🌐 Author: Kevin Ford
🔰 Level: Beginner
⏰ Duration: 4h 17m
📋 Topics: Mobile Application Development, Machine Learning
🔗 Join Machine Learning for more courses | 6 181 |
| 18 |  +1FREE MIT books on AI and Machine Learning: 📚🤖
1. Foundations of Machine Learning cs.nyu.edu/~mohri/mlbook/
2. Understanding Deep Learning udlbook.github.io/udlbook/
3. Introduction to Machine Learning Systems ❯ Vol 1: mlsysbook.ai/vol1/assets/do ❯ Vol 2: mlsysbook.ai/vol2/assets/do
4. Algorithms for ML algorithmsbook.com
5. Deep Learning deeplearningbook.org
6. Reinforcement Learning andrew.cmu.edu/course/10-703/
7. Distributional Reinforcement Learning direct.mit.edu/books/oa-monog
8. Multi Agent Reinforcement Learning marl-book.com
9. Agents in the Long Game of AI direct.mit.edu/books/oa-monog
10. Fairness and Machine Learning fairmlbook.org
11. Probabilistic Machine Learning
❯ Part 1 : probml.github.io/pml-book/book1
❯ Part 2 : probml.github.io/pml-book/book2 | 8 028 |
| 19 |  🧠 10 Graph Algorithms Visualized | 7 191 |
| 20 | 💡 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 | 2 944 |
