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 71 673 subscribers, ranking 1 773 in the Technologies & Applications category and 4 477 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 71 673 subscribers.
According to the latest data from 16 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 1 172 over the last 30 days and by 17 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 10.53%. Within the first 24 hours after publication, content typically collects 2.39% reactions from the total number of subscribers.
- Post reach: On average, each post receives 7 550 views. Within the first day, a publication typically gains 1 716 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 17.
- 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 17 July, 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.
71 673
Subscribers
+1724 hours
+4207 days
+1 17230 days
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| Date | Subscriber Growth | Mentions | Channels | |
| 17 July | +5 | |||
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| 14 July | +43 | |||
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| 12 July | +33 | |||
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| 03 July | +31 | |||
| 02 July | +37 | |||
| 01 July | +40 |
Channel Posts
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 |
| 7 | 🔅 PREMIUM CHANNELS
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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. 📘✨
Over 7 years of AI/ML experience distilled into intuition-first, no hand-waving explanations that connect the concepts in a way that actually sticks. 🧠🔗
What it covers:
- Vectors, linear algebra, calculus, and optimization 📐📉
- Classical machine learning and deep learning 🤖
- Transformer architectures and LLMs 🦄
- Efficient architectures, quantization, and distillation ⚡️
- CUDA, GPU programming, and SIMD 🚀
- 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 |
| 20 |  💡 Welcome to The Premium Vault – 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.
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1⃣ Tutorials, and resources across various premium niches
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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 | 3 253 |
