Machine Learning

Kanalga Telegram’da o‘tish

Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Ko'proq ko'rsatish

Tarmoq:Machine Learning with Python Suriya231 Texnologiyalar & Aralashmalar3 384...

📈 Telegram kanali Machine Learning analitikasi

Machine Learning (@machinelearning9) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 40 106 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 384-o'rinni va Suriya mintaqasida 231-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 40 106 obunachiga ega bo‘ldi.

24 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 401 ga, so‘nggi 24 soatda esa 38 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 1.96% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.16% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 788 marta ko‘riladi; birinchi sutkada odatda 465 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 2 ta reaksiya keladi.
Tematik yo‘nalishlar: Kontent distance, insidead, gpu, learning, degree kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 25 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

40 106

Obunachilar

+3824 soatlar

+637 kunlar

+40130 kunlar

788

Post ko'rishlar

~ 46524 soatlar

~ 62048 soatlar

1.96%

Muloqot nisbati

~ 3

Kuniga postlar

Ads index

beta

Postlar arxiv

40 106

📌 Why Healthcare Leads in Knowledge Graphs 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-18 | ⏱️ Read time: 9 min read How science, regulation, collaboration, and public funding shaped the world’s most mature semantic infrastructure #DataScience #AI #Python

40 106

Adakah anda merasakan analisis anda sentiasa kekurangan rangka kerja？Kami telah menubuhkan forum perbincangan mendalam yang memberi t Adakah anda merasakan analisis anda sentiasa kekurangan rangka kerja？Kami telah menubuhkan forum perbincangan mendalam yang memberi t Sponsored By WaybienAds

40 106

📌 The Hidden Opportunity in AI Workflow Automation with n8n for Low-Tech Companies 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-18 | ⏱️ Read time: 14 min read How to use n8n with multimodal AI and optimisation tools to help companies with low… #DataScience #AI #Python

40 106

Repost from Machine Learning with Python

Best GitHub repositories to learn AI from scratch in 2026:

1. Andrej Karpathy https://github.com/karpathy/nn-zero-to-hero 2. Hugging Face Transformers https://github.com/huggingface/transformers 3. FastAI/fastbook https://github.com/fastai/fastbook 4. Made-With-ML https://github.com/GokuMohandas/Made-With-ML 5. ML System Design https://github.com/chiphuyen/machine-learning-systems-design 6. Awesome Generative AI guide https://github.com/aishwaryanr/awesome-generative-ai-guide 7. Dive into Deep Learning https://github.com/d2l-ai/d2l-en 🪞 @codeprogrammer Like & Share

40 106

📌 A Geometric Method to Spot Hallucinations Without an LLM Judge 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-17 | ⏱️ Read time: 7 min read Imagine a flock of birds in flight. There’s no leader. No central command. Each bird… #DataScience #AI #Python

40 106

📌 Data Poisoning in Machine Learning: Why and How People Manipulate Training Data 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-17 | ⏱️ Read time: 14 min read Do you know where your data has been? #DataScience #AI #Python

40 106

Repost from Machine Learning with Python

🤖 Machine Learning Tutorials Repository 1. Python 2. Computer Vision: Techniques, algorithms 3. NLP 4. Matplotlib 5. NumPy 6. Pandas 7. MLOps 8. LLMs 9. PyTorch/TensorFlow git clone https://github.com/patchy631/machine-learning 🔗 GitHub: https://github.com/patchy631/machine-learning/tree/main ⭐️ https://t.me/DataScienceT

40 106

📌 The Great Data Closure: Why Databricks and Snowflake Are Hitting Their Ceiling 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-16 | ⏱️ Read time: 13 min read Acquisitions, venture, and an increasingly competitive landscape all point to a market ceiling #DataScience #AI #Python

40 106

📌 From RGB to Lab: Addressing Color Artifacts in AI Image Compositing 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-16 | ⏱️ Read time: 13 min read A multi-tier approach to segmentation, color correction, and domain-specific enhancement #DataScience #AI #Python

40 106

📌 Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-16 | ⏱️ Read time: 18 min read Why your final LLM layer is OOMing and how to fix it with a custom… #DataScience #AI #Python

40 106

👍 Top Channels on Telegram 🌟

40 106

Repost from Machine Learning with Python

YOLO Training Template Manual data labeling has become significantly more convenient. Now the process looks like in the usual labeling systems - you just outline the object with a frame and a bounding box is immediately created. The platform allows: • to upload your own dataset • to label manually or auto-label via DINOv3 • to enrich the data if desired • to train a #YOLO model on your own data • to run inference immediately • to export to ONNX or NCNN, which ensures compatibility with edge hardware and smartphones All of this is available for free and can already be tested on #GitHub. Repo: https://github.com/computer-vision-with-marco/yolo-training-template https://t.me/CodeProgrammer

40 106

📌 Maximum-Effiency Coding Setup 🗂 Category: PROGRAMMING 🕒 Date: 2026-01-16 | ⏱️ Read time: 9 min read Learn how to be a more efficient programmer #DataScience #AI #Python

40 106

📌 Do You Smell That? Hidden Technical Debt in AI Development 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-15 | ⏱️ Read time: 14 min read Why speed without standards creates fragile AI products #DataScience #AI #Python

40 106

📌 The 2026 Goal Tracker: How I Built a Data-Driven Vision Board Using Python, Streamlit, and Neon 🗂 Category: PRODUCTIVITY 🕒 Date: 2026-01-15 | ⏱️ Read time: 8 min read Designing a centralized system to track daily habits and long-term goals #DataScience #AI #Python

40 106

📌 How to Run Coding Agents in Parallel 🗂 Category: AGENTIC AI 🕒 Date: 2026-01-15 | ⏱️ Read time: 8 min read Get the most out of Claude Code #DataScience #AI #Python

40 106

📌 When Shapley Values Break: A Guide to Robust Model Explainability 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-15 | ⏱️ Read time: 9 min read Shapley Values are one of the most common methods for explainability, yet they can be… #DataScience #AI #Python