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Artificial Intelligence
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🔒 Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM
Mostrar más📈 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 70 715 suscriptores, ocupando la posición 1 835 en la categoría Tecnologías y Aplicaciones y el puesto 4 624 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 70 715 suscriptores.
Según los últimos datos del 24 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 941, y en las últimas 24 horas de 47, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.08%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.48% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 5 008 visualizaciones. En el primer día suele acumular 1 044 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 25 junio, 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.
70 715
Suscriptores
+4724 horas
+2227 días
+94130 días
Archivo de publicaciones
70 736
🔅 Machine Learning Foundations: Probability
📝 Get an in-depth introduction to probability, find out why its a prerequisite for machine learning, and learn how to use it to design and implement machine learning algorithms.
🌐 Author: Terezija Semenski
🔰 Level: Beginner
⏰ Duration: 1h 24m
📋 Topics: Probability, Machine Learning
🔗 Join Machine Learning for more courses
70 736
AI Chatbots Are Making Up Fake Sources Called Grokipedia
Users and researchers have noticed that some AI chatbots sometimes generate invented source names — like “Grokipedia” — when answering questions, giving the impression of real references that don’t actually exist.
These fabricated citations aren’t reliable and can mislead people trying to verify information, especially in areas like history, science, or current events.
The issue highlights a common limitation in many generative models: they can present plausible-looking but false reference material as if it were real.
70 736
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🔍 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!
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🤝 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
70 736
🔅 Machine Learning with Python: k-Means Clustering
📝 Learn the basics of k-means clustering, one of the most popular unsupervised machine learning approaches.
🌐 Author: Frederick Nwanganga
🔰 Level: Intermediate
⏰ Duration: 50m
📋 Topics: k-means clustering, Machine Learning, Python
🔗 Join Machine Learning for more courses
70 736
🔍 Machine Learning Cheat Sheet 🔍
1. Key Concepts:
- Supervised Learning: Learn from labeled data (e.g., classification, regression).
- Unsupervised Learning: Discover patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- Reinforcement Learning: Learn by interacting with an environment to maximize reward.
2. Common Algorithms:
- Linear Regression: Predict continuous values.
- Logistic Regression: Binary classification.
- Decision Trees: Simple, interpretable model for classification and regression.
- Random Forests: Ensemble method for improved accuracy.
- Support Vector Machines: Effective for high-dimensional spaces.
- K-Nearest Neighbors: Instance-based learning for classification/regression.
- K-Means: Clustering algorithm.
- Principal Component Analysis(PCA)
3. Performance Metrics:
- Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC.
- Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R^2 Score.
4. Data Preprocessing:
- Normalization: Scale features to a standard range.
- Standardization: Transform features to have zero mean and unit variance.
- Imputation: Handle missing data.
- Encoding: Convert categorical data into numerical format.
5. Model Evaluation:
- Cross-Validation: Ensure model generalization.
- Train-Test Split: Divide data to evaluate model performance.
6. Libraries:
- Python: Scikit-Learn, TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib.
- R: caret, randomForest, e1071, ggplot2.
7. Tips for Success:
- Feature Engineering: Enhance data quality and relevance.
- Hyperparameter Tuning: Optimize model parameters (Grid Search, Random Search).
- Model Interpretability: Use tools like SHAP and LIME.
- Continuous Learning: Stay updated with the latest research and trends.
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🔅 Machine Learning with Python: Association Rules
📝 Explore the unsupervised machine learning approach known as association rules, as well as a step-by-step guide on how to use the approach for market basket analysis in Python.
🌐 Author: Frederick Nwanganga
🔰 Level: Intermediate
⏰ Duration: 1h 27m
📋 Topics: Machine Learning, Python
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