Artificial Intelligence
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π Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM
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Network:π 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 71 648 subscribers, ranking 1 800 in the Technologies & Applications category and 4 421 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 71 648 subscribers.
According to the latest data from 26 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 -29 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.02%. Within the first 24 hours after publication, content typically collects 1.58% reactions from the total number of subscribers.
- Post reach: On average, each post receives 4 316 views. Within the first day, a publication typically gains 1 129 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|.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βπ Welcome Artificial Intelligence Channel
Buy ads: https://telega.io/c/Artificial_Intelligence_COMβ
Thanks to the high frequency of updates (latest data received on 27 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.
71 648
Subscribers
-2924 hours
-1937 days
-38430 days
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Channel Posts
| 2 |  π
Responsible AI and Application Development
π Learn all of the steps of creating an AI application with machine learning (ML), ensuring fairness and mitigating biases throughout the process.
π Author: Laurence Moroney
π° Level: Intermediate
β° Duration: 1h 37m
π Topics: AI Software Development, Responsible AI, Artificial Intelligence
π Join Machine Learning for more courses | 1 766 |
| 3 |  +2Machine Learning vs. Deep Learning | 2 486 |
| 4 |  +3Machine Learning vs. Deep Learning | 2 212 |
| 5 |  +3Machine Learning vs. Deep Learning | 1 725 |
| 6 |  +3Machine Learning vs. Deep Learning
π€ Machine Learning: Requires engineered features + clean data.
Examples: Credit scoring (Logistic Regression), sales forecast (Decision Trees).
π§ Deep Learning: Takes raw data (pixels, text, audio). It learns features end-to-end via hidden layers.
Examples: Self-driving cars (CNN), voice assistants (RNN), LLMs (Transformers).
ML needs your brain. DL builds its own. | 1 773 |
| 7 | π€ IMPORTANT AI TERMS & TOOLS YOU SHOULD KNOW π§
If you want to understand modern AI better, these are some of the most important terms and tools to know.
π§ 1. Generative AI
AI that can create new content such as:
βοΈ Text
βοΈ Images
βοΈ Audio
βοΈ Video
βοΈ Code
Examples include ChatGPT, Gemini and Claude.
π¬ 2. Large Language Model (LLM)
An AI model trained on large amounts of text to understand and generate human-like language.
LLMs power many modern AI chatbots and writing tools.
π 3. Prompt
A prompt is the instruction or input you give to an AI system.
A clear and specific prompt generally helps you get more useful results.
π― 4. Prompt Engineering
The practice of designing and improving prompts to get better results from AI models.
It can involve giving context, defining the task, specifying the format and providing examples.
π’ 5. Tokens
AI models process text as tokens rather than simply reading complete words.
A token can represent a word, part of a word, punctuation, or other pieces of text.
π 6. Context Window
The amount of information an AI model can consider within a conversation or request.
A larger context window allows a model to work with more text and information at once.
𧩠7. Fine-Tuning
A process of further training an existing AI model on specific data to improve its performance for a particular task or domain.
π 8. RAG
RAG stands for Retrieval-Augmented Generation.
It allows an AI system to retrieve relevant information from external sources and use it when generating an answer.
This can be useful for working with company documents, knowledge bases and other specific information.
π€ 9. AI Agent
An AI agent is a system that can use AI to plan and perform multiple steps toward a goal, sometimes using external tools or applications.
For example, an agent could research information, analyze it and then prepare a report.
π 10. API
An API allows different software systems to communicate with each other.
AI APIs allow developers to integrate AI capabilities into websites, apps and other software.
β‘οΈ 11. Machine Learning
A branch of AI where systems learn patterns from data and use those patterns to make predictions or decisions.
π§ 12. Deep Learning
A type of machine learning that uses neural networks with multiple layers to learn complex patterns.
It is widely used in areas such as computer vision, speech and language processing.
πΈ 13. Neural Network
A machine-learning model inspired by the way biological neural networks are structured.
Neural networks are a foundation of many modern AI systems.
π 14. Multimodal AI
AI that can work with multiple types of information, such as:
βοΈ Text
βοΈ Images
βοΈ Audio
βοΈ Video
This allows AI systems to understand and generate different forms of content.
π 15. AI Tools You Should Know
Some popular categories of AI tools include:
βοΈ ChatGPT β AI assistant, writing, research and problem-solving
βοΈ Gemini β AI assistant and multimodal tasks
βοΈ Claude β Writing, analysis and coding
βοΈ Perplexity β AI-powered search and research
βοΈ Midjourney β AI image generation
βοΈ Runway β AI video generation and editing
βοΈ GitHub Copilot β AI-assisted coding
βοΈ ElevenLabs β AI voice generation | 2 382 |
| 8 |  7 Layers of LLM Stack | 2 698 |
| 9 |  4 learning paradigms in machine learning, explained visually:
1. Transfer Learning
2. Fine-tuning
3. Multi-task Learning
4. Federated Learning | 3 930 |
| 10 |  π‘ Your Gateway to Exclusive Content
π 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.
π¦ Whatβs Inside?
1β£ Tutorials, and resources across various premium sites
π’ Movies, TV Shows and Documentaries
π’ Premium Applications, fully featured, paid-tier software and productivity tools
γ°οΈγ°οΈγ°οΈγ°οΈγ°οΈγ°οΈγ°οΈγ°οΈγ°οΈ
π« 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 β shared only with our inner circle of premium subscribers.
π https://t.me/ThePremiumVault/4 | 3 860 |
| 11 | π±Machine Learning
π±RAG Tuned AIs with the Cohere API Platform | 4 175 |
| 12 |  π
RAG Tuned AIs with the Cohere API Platform
π Learn how to develop RAG-based AI tuning with Cohere's API platform, optimizing models for various cloud environments.
π Author: Ray Villalobos
π° Level: General
β° Duration: 21m
π Topics: Retrieval-Augmented Generation
π Join Machine Learning for more courses | 4 150 |
| 13 |  π§ Understanding Machine learning algorithms | 4 798 |
| 14 |  Not all Machine Learning algorithms learn the same way.
Choosing the right one can make or break your model.
This visual breaks down:
πΉ How ML algorithms differ
πΉ Supervised vs Unsupervised learning
πΉ Model complexity (simple β deep)
πΉ Popular algorithm families
πΉ Real-world business impact
π From Linear Models to Tree-Based Models to Neural Networks, every algorithm is a trade-off between:
β
Accuracy
β‘οΈ Speed
𧩠Interpretability
π Real example:
The right algorithm improved customer churn prediction by +15% accuracy - turning lost users into retained customers. | 5 455 |
| 15 |  Exploring Data & AI Career Paths: Which Role Fits You?
The world of data offers diverse opportunities β hereβs a breakdown of key roles in AI and data science:
1οΈβ£ ML Engineers
- Design and optimize data pipelines
- Work closely with data engineers
- Focus on scalable ML systems
2οΈβ£ ML Ops Specialists
- Manage databases & data tools
- Master storytelling & visualization
- Ensure smooth model deployment
3οΈβ£ Data & Analytics Experts
- Apply stats & ML modeling
- Run experiments & A/B tests
- Deliver business insights
- Specialize in metrics & reporting
Why This Matters
β
High demand across industries
β
Competitive salaries in tech
β
Opportunities to work with cutting-edge AI | 4 954 |
| 16 | π
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π±How to Use AI Reasoning Models: Practical Applications with Hands-On Exercises | 5 538 |
| 18 |  π
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π Develop the know-how and skills to leverage OpenAI's o-Series models for advanced reasoning tasks, business applications, and scientific research.
π Author: Anurag Karuparti
π° Level: Intermediate
β° Duration: 1h 59m
π Topics: Azure AI Studio, OpenAI API, Large Language Models
π Join Machine Learning for more courses | 5 403 |
| 19 |  Normalization vs Standardization: Why Theyβre Not the Same
People treat these two as interchangeable. theyβre not.
π Normalization (Min-Max scaling):
Compresses values to 0β1.
Useful when magnitude matters (pixel values, distances).
π Standardization (Z-score):
Centers data around mean=0, std=1.
Useful when distribution shape matters (linear/logistic regression, PCA).
π Key idea:
Normalization preserves relative proportions.
Standardization preserves statistical structure.
Pick the wrong one, and your modelβs geometry becomes distorted. | 6 157 |
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217k| π° Linkedin Learning
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133k| π° Web Development
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