Artificial Intelligence
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🔒 Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM
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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 72 419 subscribers, ranking 1 719 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 72 419 subscribers.
According to the latest data from 30 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 405 over the last 30 days and by -37 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.63%. Within the first 24 hours after publication, content typically collects 1.94% reactions from the total number of subscribers.
- Post reach: On average, each post receives 4 801 views. Within the first day, a publication typically gains 1 407 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 11.
- 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 31 August, 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.
72 419
Subscribers
-3724 hours
-1227 days
+40530 days
Posts Archive
72 410
📱Artificial Intelligence and Machine Learning
📱Machine Learning and AI Foundations: Clustering and Association
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🔅 Machine Learning and AI Foundations: Clustering and Association
📝 Learn how to use cluster analysis, association rules, and anomaly detection algorithms for unsupervised learning.
🌐 Author: Keith McCormick
🔰 Level: Intermediate
⏰ Duration: 3h 33m
📋 Topics: Machine Learning, Artificial Intelligence
🔗 Join Artificial Intelligence and Machine Learning for more courses
72 410
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🔰 2hrs on top & 8hrs in channel!
72 410
📱Artificial Intelligence and Machine Learning
📱Choosing the Right ML Approach for Your Business Case
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🔅 Choosing the Right ML Approach for Your Business Case
📝 Learn the system components of machine learning (ML), their function in the AI ecosystem, and how to choose the best approach for your business pipeline.
🌐 Author: Lyron Andrews
🔰 Level: Intermediate
⏰ Duration: 1h 42m
📋 Topics: Machine Learning, Artificial Intelligence
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030k| 🔰 Coding Interview
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022k| 🔰 The Coding Space
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72 410
🔗 Machine Learning, Simplified
Ever wondered what Machine Learning really means and how it impacts your everyday life?
ML is not just about fancy algorithms—it's about how machines learn like humans to make decisions, automate tasks, and personalize your digital experiences. 🔍✨
Here are real-world use cases you interact with daily:
🔹 Generative AI → ChatGPT, Midjourney
🔹 Speech Recognition → Siri, Alexa
🔹 Computer Vision → Face ID, Self-driving cars
🔹 RPA & Stock Trading Bots → Automating workflows & finance!
72 410
🔅 GraphRAG Essential Training
📝 Learn how to build robust AI applications by creating knowledge graphs for retrieval-augmented generation (RAG) in Python using LangChain and Neo4j.
🌐 Author: Dr. Clair Sullivan
🔰 Level: Intermediate
⏰ Duration: 1h 39m
📋 Topics: Retrieval-Augmented Generation, Knowledge Graph Augmentation, Knowledge Graphs
🔗 Join Artificial Intelligence and Machine Learning for more courses
72 410
🔰 Why Python is a Must-Have Skill?
If you're diving into programming or data science, mastering Python is essential! Its versatility and simplicity make it the go-to language across industries.◆ Powerful and Versatile From web development to data analysis, Python’s broad libraries and frameworks adapt to almost any project. ◆ Data-Driven Python, combined with libraries like Pandas and NumPy, allows you to analyze and manipulate datasets efficiently. ◆ Automate the Boring Stuff Automate repetitive tasks, streamline workflows, and boost productivity with Python’s easy-to-use scripts. ◆ AI and Machine Learning With frameworks like TensorFlow and Scikit-learn, Python is at the forefront of AI, enabling you to build predictive models and explore deep learning. ◆ Readable and Beginner-Friendly Python’s simple syntax makes it easy to learn, even for beginners, without sacrificing power and functionality. ◆ Community Support Backed by a massive global community, Python is constantly evolving, with new libraries and resources available at your fingertips.
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+1
⚡️ Agentic Reward Modeling is a fresh project from THU-KEG, the goal of which is to rethink the approach to training agent systems.
This tool aims to develop reward methods where the agent does not simply follow commands, but learns to understand its actions in the context of more complex tasks and long-term goals.
Key Features:
- Instead of standard RL methods, where rewards often depend on pre-set criteria, the emphasis here is on developing more complex strategies that adapt to changing environments and goals.
- The tool helps model rewards in such a way that the agent can independently adjust its actions, learn from mistakes and, ultimately, demonstrate more “human” decision making.
- Developers can use this approach in multi-agent systems and complex tasks where dynamic assessment of the effectiveness of actions is important.
This tool is interesting not only for its theoretical potential, but also for its practical applications in the field of creating more autonomous and intelligent systems. Agentic Reward Modeling opens up new possibilities for studying agents that can learn in real time, which makes it promising for further research and integration into real applications.
▪️Paper: https://arxiv.org/abs/2502.19328
▪️Code: https://github.com/THU-KEG/Agentic-Reward-Modeling
