Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
Machine Learning with Python
Kanalga Telegram’da o‘tish
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
Ko'proq ko'rsatish📈 Telegram kanali Machine Learning with Python analitikasi
Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 67 809 obunachidan iborat bo'lib, Taʼlim toifasida 2 416-o'rinni va Hindiston mintaqasida 5 038-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 67 809 obunachiga ega bo‘ldi.
09 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 70 ga, so‘nggi 24 soatda esa 10 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 2.94% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.44% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 1 997 marta ko‘riladi; birinchi sutkada odatda 1 652 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 7 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 10 Iyun, 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.
67 809
Obunachilar
+1024 soatlar
+127 kunlar
+7030 kunlar
Postlar arxiv
Repost from Github Top Repositories
🔥 Trending Repository: awesome-deeplearning-resources
📝 Description: Deep Learning and deep reinforcement learning research papers and some codes
🔗 Repository URL: https://github.com/endymecy/awesome-deeplearning-resources
📖 Readme: https://github.com/endymecy/awesome-deeplearning-resources#readme
📊 Statistics:
🌟 Stars: 2.9K stars
👀 Watchers: 221
🍴 Forks: 666 forks
💻 Programming Languages: Not available
🏷️ Related Topics:
#nlp #video #reinforcement_learning #deep_learning #neural_network #code #paper #corpus #modelzoo================================== 🧠 By: https://t.me/DataScienceN
Repost from Github Top Repositories
🔥 Trending Repository: awesome-transformer-nlp
📝 Description: A curated list of NLP resources focused on Transformer networks, attention mechanism, GPT, BERT, ChatGPT, LLMs, and transfer learning.
🔗 Repository URL: https://github.com/cedrickchee/awesome-transformer-nlp
📖 Readme: https://github.com/cedrickchee/awesome-transformer-nlp#readme
📊 Statistics:
🌟 Stars: 1.1K stars
👀 Watchers: 41
🍴 Forks: 131 forks
💻 Programming Languages: Not available
🏷️ Related Topics:
#nlp #natural_language_processing #awesome #transformer #neural_networks #awesome_list #llama #transfer_learning #language_model #attention_mechanism #bert #gpt_2 #xlnet #pre_trained_language_models #gpt_3 #gpt_4 #chatgpt================================== 🧠 By: https://t.me/DataScienceM
Repost from Github Top Repositories
🔥 Trending Repository: SemanticSegmentation_DL
📝 Description: Resources of semantic segmantation based on Deep Learning model
🔗 Repository URL: https://github.com/tangzhenyu/SemanticSegmentation_DL
📖 Readme: https://github.com/tangzhenyu/SemanticSegmentation_DL#readme
📊 Statistics:
🌟 Stars: 1.1K stars
👀 Watchers: 77
🍴 Forks: 315 forks
💻 Programming Languages: Jupyter Notebook - Python - Shell - sed
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.me/DataScienceM
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Python library that adds Generative AI capabilities to Pandas!
PandasAI analyzes complex data frames and plot visualizations just by using natural language.
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python-docx: Create and Modify Word Documents #python
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
pip install python-docxfrom docx import Document
document = Document()
document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
document.save("dark-and-stormy.docx")
document = Document("dark-and-stormy.docx")
document.paragraphs[0].text
'It was a dark and stormy night.'