AI and Machine Learning
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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 94 728 obunachidan iborat bo'lib, Taʼlim toifasida 1 530-o'rinni va Hindiston mintaqasida 3 007-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 94 728 obunachiga ega bo‘ldi.
16 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 896 ga, so‘nggi 24 soatda esa 30 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 10.17% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.68% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 9 631 marta ko‘riladi; birinchi sutkada odatda 2 538 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 17 Iyul, 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.
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| 05 Iyul | +33 | |||
| 04 Iyul | +15 | |||
| 03 Iyul | +18 | |||
| 02 Iyul | +22 | |||
| 01 Iyul | +32 |
Kanal postlari
📄 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
| 2 | 📱Artificial intelligence
📱Build with AI: Advanced Production-Ready Gradio Applications | 4 227 |
| 3 |  🔅 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 | 4 181 |
| 4 |  🚀 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. | 6 476 |
| 5 |  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 | 3 505 |
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🔰 2hrs on top & 8hrs in channel! | 1 959 |
| 7 |  ⭐️ Want to become an AI/ML Engineer?
Here’s a simple 15-step roadmap – from learning Python to building real-world projects. | 8 005 |
| 8 | GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model
The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra.
What’s inside:
🔘A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale;
🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer;
🔘GatedNorm: normalization with an explicit gate that controls signal magnitude across features;
🔘Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load;
🔘Two MTP heads, enabling up to 2.2x faster generation;
🔘FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels;
🔘A new online RL stage after SFT and DPO.
Results:
🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks:
🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size;
🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%.
The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team.➡️ HuggingFace | 3 381 |
| 9 |  📕 RAG Pipeline vs Self RAG vs Agentic RAG | 9 693 |
| 10 | 📱Artificial intelligence
📱AI Model Compression Techniques: Building Cheaper, Faster, and Greener AI | 11 887 |
| 11 |  🔅 AI Model Compression Techniques: Building Cheaper, Faster, and Greener AI
📝 Learn how to make AI models faster, smaller, and more sustainable with practical techniques like pruning, quantization, and distillation.
🌐 Author: Tejas Chopra
🔰 Level: Intermediate
⏰ Duration: 1h 55m
📋 Topics: Model Compression, Artificial Intelligence
🔗 Join Artificial intelligence for more courses | 11 716 |
| 12 |  🐶 ASO Corgi — platform for the App Store developers.
Find the keywords your apps and competitors rank for, and track positions across every country in one place.
🔑 Keyword research: by topic, by your app's languages, from App Store suggestions, by competitors, and with AI analysis.
• Rankings by country — history, charts, demand score (0–100)
• Global search across any App Store storefront
• ASO assistant builds your listing for each locale
• App Store top charts for any country
🎁 14 days of Pro, free 👇 (no card required)
https://asocorgi.com/?promo=promo14&utm_source=machine_learning_courses&utm_medium=telegram&utm_campaign=launch | 1 978 |
| 13 | Designing
Machine Learning
Systems.pdf | 14 015 |
| 14 |  📚 Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications | 13 940 |
| 15 |  💡 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
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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 | 2 634 |
| 16 |  📢 Hugging Face is now integrated with Kaggle Notebooks
Starting today, Kaggle users can directly use any Hugging Face models in their notebooks — without manual downloads, token setup, or additional libraries.
🤝 Hugging Face and Kaggle platforms announced a partnership that will allow competition participants and researchers to work with the latest SOTA models literally "out of the box."
🔥 This is just the first step: teams are already working on further integration to make working with HF models even more convenient within the Kaggle ecosystem.
🔗 You can try it right now — support is already included in the Kaggle Notebooks environment.
https://huggingface.co/blog/kaggle-integration | 14 764 |
| 17 |  🔎 Using TensorFlow Object Detection API with OpenVINO™
🛠 TensorFlow, or TF for short, is an open-source framework for machine learning.
🔰 The TensorFlow Object Detection API is an open-source computer vision framework built on top of TensorFlow.
🔰It is used for building object detection and instance segmentation models that can localize multiple objects in the same image.
🔰TensorFlow Object Detection API supports various architectures and models, which can be found and downloaded from the TensorFlow Hub.
🌐 Links: Github | 13 556 |
| 18 | 📦 Exercise Files | 13 751 |
| 19 | 📱Artificial intelligence
📱🆕 Applied AI: Getting Started with Hugging Face Transformers | 13 452 |
| 20 | 📱Artificial intelligence
📱Applied AI: Getting Started with Hugging Face Transformers | 14 486 |
