AI and Machine Learning
Kanalga Telegram’da o‘tish
Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses
Ko'proq ko'rsatish📈 Telegram kanali AI and Machine Learning analitikasi
AI and Machine Learning (@machine_learning_courses) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 95 282 obunachidan iborat bo'lib, Taʼlim toifasida 1 507-o'rinni va Hindiston mintaqasida 2 936-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 95 282 obunachiga ega bo‘ldi.
03 Sentabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 392 ga, so‘nggi 24 soatda esa 32 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 7.64% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.40% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 7 276 marta ko‘riladi; birinchi sutkada odatda 2 290 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 18 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent learning, llm, linkedin, linux, udemy kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more!
Buy ads: https://telega.io/c/machine_learning_courses”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 04 Sentabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
95 282
Obunachilar
+3224 soatlar
+1087 kun
+39230 kun
Postlar arxiv
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Key Nodes in n8n
Most people think AI automation is complex, but with n8n it comes down to just 7 building blocks.1️⃣ Code Node → custom logic 2️⃣ HTTP Request → connect any API 3️⃣ Edit Fields → clean data 4️⃣ IF Node → conditional paths 5️⃣ Switch Node → handle multiple cases 6️⃣ Loop Over Items → process lists 7️⃣ Error Handling → keep workflows alive n8n makes it simple: drag, drop, connect.
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🔅 Hands-On AI: Building Your First LLM-Powered App
📝 Get started building apps powered by large language models (LLMs) in this hands-on, skills-based course for beginners.
🌐 Author: Han-chung Lee
🔰 Level: Beginner
⏰ Duration: 1h 14m
📋 Topics: AI Software Development, Large Language Models, Artificial Intelligence
🔗 Join Artificial intelligence for more courses
95 294
All major LLMs, one login: glbgpt.com
GPT-5 · Claude · Gemini · Grok · DeepSeek. Crypto top-up (USDT), pay-as-you-go, no sub required.
→ https://tglink.io/ad3c29e3f95e5e
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🔢 Stages of LLM Training
Training a Large Language Model isn’t a single step—it’s a journey through multiple stages that shape how it understands, follows, and reasons.
Here’s the progression:
Stage 0 → A randomly initialized model, just noise with potential.
Stage 1 → Pre-Training, where it absorbs patterns from massive text data.
Stage 2 → Instruction Fine-Tuning, aligning it to follow human-written instructions.
Stage 3 → Preference Fine-Tuning, refining it to generate responses people actually prefer.
Stage 4 → Reasoning Fine-Tuning, pushing it to think more logically and solve complex problems.
From raw parameters to advanced reasoning, each stage transforms the model into something smarter, more helpful, and more aligned with human goals.
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🧠 Code with an AI agent — inside Telegram
BrainDaemon is a private AI coding workspace (Mini App):
• Cloud machine + real tools per chat
• Build multi-file projects, not just paste snippets
• Live reasoning while it works with you
Open the Mini App and start building:
👉 https://t.me/BrainDaemonBot
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📱Artificial intelligence
📱Mastering Reasoning Models: Algorithms, Optimization, and Applications
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🔅 Mastering Reasoning Models: Algorithms, Optimization, and Applications
📝 Learn how to build and optimize reasoning-specialized LLMs through mastery of test-time compute scaling and GRPO.
🌐 Author: Nayan Saxena
🔰 Level: Advanced
⏰ Duration: 1h 20m
📋 Topics: Large Language Models, Artificial Intelligence
🔗 Join Artificial intelligence for more courses
95 294
💡 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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If you're building AI agents, you should get familiar with these 3 common agent/workflow patterns.
Let's break it down.
🔹 Reflection
You give the agent an input.
The agent then "reflects" on its output, and based on feedback, improves and refines.
Ideal tools to use:
- Base model (e.g. GPT-4o)
- Fine-tuned model (to give feedback)
- n8n to set up the agent.
🔹 RAG-based
You give the agent a task.
The agent has the ability to query an external knowledge base to retrieve specific information needed.
Ideal tools to use:
- Vector Database (e.g. Pinecone).
- UI-based RAG (Aidbase is the #1 tool).
- API-based RAG (SourceSync is a new player on the market, highly promising).
🔹 AI Workflow
This is a "traditional" automation workflow that uses AI to carry out subtasks as part of the flow.
Ideal tools to use:
- n8n to handle the workflow.
- GPT-4o, Claude, or other models that can be accessed through API (basic HTTP requests).
If you can master these 3 patterns well, you can solve a very broad range of different problems.
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📄 ColQwen2: document search considering visual layout
ColQwen2 is a modified version of the ColPali model designed to search documents by their visual features, not just by text.
🔧 How it works:
• Each page is processed as an image
• Qwen2-VL is used to extract not only text but also tables, charts, layout
• Multivector embeddings are created
• Search is based on comparing these vectors (late interaction)
📌 Why this is needed:
This approach helps to find the right documents more accurately — especially if they contain complex structure, tables, or non-standard format.
Suitable for:
– PDF files
– Scanned documents
– Presentations and reports with visual elements
https://huggingface.co/docs/transformers/main/en/model_doc/colqwen2
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🔅 Build with AI: Advanced Production-Ready Gradio Applications
📝 Build advanced, production-ready AI apps with Gradio, LangChain, Docker, and cloud deployment.
🌐 Author: Deepak Goyal
🔰 Level: Advanced
⏰ Duration: 1h 19m
📋 Topics: Artificial Intelligence, Application Development
🔗 Join Artificial intelligence for more courses
95 294
🚀 Top AI Algorithms & Their Use-Cases
A quick reference to essential AI algorithms and how they’re applied in real projects:
Supervised Learning
- Linear Regression: Predicting house prices based on features
- Logistic Regression: Spam email classification
- Decision Trees: Customer churn prediction
- Random Forest: Stock price prediction
- Gradient Boosting: Credit scoring for loan approval
- K-Nearest Neighbors (KNN): Movie recommendation systems
- Naive Bayes: Text classification (e.g., spam or not)
- Support Vector Machines (SVM): Handwriting recognition in digit datasets
Unsupervised Learning
- K-Means Clustering: Customer segmentation for marketing
- Principal Component Analysis (PCA): Image compression
- Gaussian Mixture Model (GMM): Anomaly detection in network security
- Association Rule Learning: Market basket analysis in retail
Deep Learning & Neural Networks
- Neural Networks: Facial recognition
- Recurrent Neural Networks (RNN): Sentiment analysis in text
- Long Short-Term Memory (LSTM): Stock market prediction
- Word Embeddings: Improving search engine relevance
Optimization & Other Techniques
- Genetic Algorithms: Optimize supply chain logistics
- Ant Colony Optimization: Solving traveling salesman problem
- Reinforcement Learning: Game playing (e.g., AlphaGo)
- Natural Language Processing (NLP): Chatbots for customer support
Each algorithm has unique strengths that power solutions across industries from finance and marketing to security and entertainment.
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Uncensored AI is here
Tired of another "I can't help you with that" from your AI?
OpenChat is an uncensored AI bot inside Telegram that answers anything you ask. Yes – absolutely anything.
It takes on the real tasks other AIs refuse: sketchy advice, a spicy story, a shady idea, a straight how-to. It also reads any photo or voice note you send. Fully private.
Try it free: t.me/theopenchat_bot
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