Artificial Intelligence

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🔒 Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM

Network:Zero To Mastery India4 348 Technologies & Applications1 737...

📈 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 72 043 subscribers, ranking 1 737 in the Technologies & Applications category and 4 348 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 72 043 subscribers.

According to the latest data from 15 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -334 over the last 30 days and by -14 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.56%. Within the first 24 hours after publication, content typically collects 2.14% reactions from the total number of subscribers.
Post reach: On average, each post receives 5 450 views. Within the first day, a publication typically gains 1 544 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 6.
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 16 September, 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.

72 043

Subscribers

-1424 hours

-1587 days

-33430 days

5 450

Post views

~ 1 54424 hours

~ 2 05048 hours

7.56%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

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Machine Learning Hyper-parameters

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📦 Exercise Files

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📱Machine Learning 📱Deep Learning: Getting Started

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🔅 Deep Learning: Getting Started 📝 Learn the basics of deep learning and get up and running with this technology. 🌐 Author: Kumaran Ponnambalam 🔰 Level: Intermediate ⏰ Duration: 1h 13m 📋 Topics: Deep Learning, Machine Learning, Artificial Intelligence 🔗 Join Machine Learning for more courses

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🔗 Top 9 Machine Learning Algorithms

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⭐️ Mayday (2026) 📅 Release Date: September 04, 2026

When a U.S. Navy pilot on a top-secret mission during the Cold War gets trapped behind enemy lines, his only chance at survival is to form an alliance with an eccentric ex-KGB agent.

🔒 VIP Exclusive This documentary is only visible to vip members. 🔗 Subscribe first: VIP Channel 🔗 Then view content: Link For more information go to: The Premium Vault

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Overview of Machine Learning

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📱Machine Learning 📱Machine Learning Foundations: Linear Algebra

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🔅 Machine Learning Foundations: Linear Algebra 📝 Explore the fundamentals of linear algebra, the mathematical foundation of machine learning algorithms. 🌐 Author: Terezija Semenski 🔰 Level: Intermediate ⏰ Duration: 1h 21m 📋 Topics: Linear Algebra, Machine Learning, Artificial Intelligence 🔗 Join Machine Learning for more courses

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📊 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.

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Scikit-learn

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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

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🤝 Confusion matrix

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📱Machine Learning 📱Execute and Evaluate Hugging Face AI Models

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🔅 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

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💡 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

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Machine Learning cheat sheet

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Top 10 Python Libraries for AI & ML

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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 📱A Hands-On Introduction to Hugging Face for Developers