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Machine Learning with Python

Machine Learning with Python

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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

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📈 Telegram kanali Machine Learning with Python analitikasi

Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 68 482 obunachidan iborat bo'lib, Taʼlim toifasida 2 406-o'rinni va Hindiston mintaqasida 4 586-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 68 482 obunachiga ega bo‘ldi.

04 Noyabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -75 ga, so‘nggi 24 soatda esa 7 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 3.38% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.57% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 2 314 marta ko‘riladi; birinchi sutkada odatda 1 073 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 9 ta reaksiya keladi.
  • Tematik yo‘nalishlar: Kontent insidead, learning, degree, evaluation, algorithm kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 05 Noyabr, 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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68 482
Obunachilar
+724 soatlar
-597 kun
-7530 kun
Postlar arxiv
Take Screenshots using Python #python http://t.me/codeprogrammer
Take Screenshots using Python #python http://t.me/codeprogrammer

The LLM Scientist Roadmap Ready to become an LLM Scientist? Follow this roadmap to unlock your potential and master the art o
The LLM Scientist Roadmap Ready to become an LLM Scientist? Follow this roadmap to unlock your potential and master the art of Large Language Models. #LLM #DataScience #Roadmap https://t.me/DataScienceM ❤️

Football Analysis in Python with Yolov8 & OpenCV Video: https://youtu.be/yJWAtr3kvPU?si=Ix9t3QiXR3jwbog7 Deep learning web ap
Football Analysis in Python with Yolov8 & OpenCV Video: https://youtu.be/yJWAtr3kvPU?si=Ix9t3QiXR3jwbog7 Deep learning web application for football analysis with Streamlit. Used material links: Project repo: https://github.com/Hmzbo/Football-Analytics-with-Deep-Learning-and-Computer-Vision Custom dataset tips: https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results/ Annotation tools: labelme: https://github.com/wkentaro/labelme cvat: https://github.com/opencv/cvat

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🖥 How to hide secret text inside an image using Python? Nothing complicated, you just need the Stegano library: # pip instal
🖥 How to hide secret text inside an image using Python? Nothing complicated, you just need the Stegano library:
# pip install stegano

from stegano import lsb
secret = lsb.hide('image.png', 'very secret text')
secret.save('secret_image.png')

print(lsb.reveal('secret_image.png'))
A quick question: how is this ability to store text in a picture implemented? How easy is it to detect such text hiding? 🖥 GitHub http://t.me/codeprogrammer ⭐️⭐️

https://t.me/dogshouse_bot/join?startapp=NiPXX1NOSf6pv9CQNPKWpQ Find out the age of your account on Telegram, the extent of its impact on the Telegram audience, and whether your account is an influential account or a normal account for Telegram

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