Artificial Intelligence
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Network:📈 Analytical overview of Telegram channel Artificial Intelligence
Channel Artificial Intelligence (@artificial_intelligence_com) in the English language segment is an active participant. Currently, the community unites 71 596 subscribers, ranking 1 800 in the Technologies & Applications category and 4 426 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 71 596 subscribers.
According to the latest data from 27 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -384 over the last 30 days and by -23 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.09%. Within the first 24 hours after publication, content typically collects 1.59% reactions from the total number of subscribers.
- Post reach: On average, each post receives 4 365 views. Within the first day, a publication typically gains 1 141 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 7.
- Thematic interests: Content is focused on key topics such as learning, linkedin, linux, udemy, 040k|.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“🔒 Welcome Artificial Intelligence Channel
Buy ads: https://telega.io/c/Artificial_Intelligence_COM”
Thanks to the high frequency of updates (latest data received on 28 October, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
71 596
Subscribers
-2324 hours
-2037 days
-38430 days
Posts Archive
71 596
🔅 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
71 596
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.
71 596
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71 596
🔍 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!
71 596
🤝 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
71 596
🔅 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
71 596
🔍 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.
71 596
🔅 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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71 596
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71 596
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