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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
Ko'proq ko'rsatish📈 Telegram kanali Machine Learning with Python analitikasi
Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 67 835 obunachidan iborat bo'lib, Taʼlim toifasida 2 428-o'rinni va Hindiston mintaqasida 5 035-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 67 835 obunachiga ega bo‘ldi.
15 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 82 ga, so‘nggi 24 soatda esa 13 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 4.40% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.74% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 2 983 marta ko‘riladi; birinchi sutkada odatda 1 177 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 5 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 16 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.
67 835
Obunachilar
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+187 kunlar
+8230 kunlar
Postlar arxiv
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Repost from Machine Learning with Python
Best Data Science Channels and groups on Telegram:
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Repost from AI & ML Papers
Best Data Science Channels and groups on Telegram:
https://t.me/addlist/8_rRW2scgfRhOTc0
Only click on OK and Will automatically add you to all channels
Please update telegram version
Google just dropped Generative AI learning path with 9 courses:
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Repost from AI & ML Papers
Best Data Science Channels and groups on Telegram:
https://t.me/addlist/8_rRW2scgfRhOTc0
Only click on OK and Will automatically add you to all channels
Please update telegram version
Repost from AI & ML Papers
Best Data Science Channels and groups on Telegram:
https://t.me/addlist/8_rRW2scgfRhOTc0
Only click on OK and Will automatically add you to all channels
Please update telegram version
80+ Jupyter Notebook tutorials on image classification, object detection and image segmentation in various domains
📌 Agriculture and Food
📌 Medical and Healthcare
📌 Satellite
📌 Security and Surveillance
📌 ADAS and Self Driving Cars
📌 Retail and E-Commerce
📌 Wildlife
Classification library
https://github.com/Tessellate-Imaging/monk_v1
Notebooks - https://github.com/Tessellate-Imaging/monk_v1/tree/master/study_roadmaps/4_image_classification_zoo
Detection and Segmentation Library
https://github.com/Tessellate-Imaging/
Monk_Object_Detection
Notebooks: https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo
https://t.me/DataScienceT
Repost from AI & ML Papers
Data Science With Python Workflow Cheat Sheet
Creator: business Science
Stars ⭐️: 75
Forked By: 38
https://github.com/business-science/cheatsheets/blob/master/Data_Science_With_Python_Workflow.pdf
https://t.me/DataScienceT
How do Transformers work?
All the Transformer models mentioned above (GPT, BERT, BART, T5, etc.) have been trained as language models. This means they have been trained on large amounts of raw text in a self-supervised fashion. Self-supervised learning is a type of training in which the objective is automatically computed from the inputs of the model. That means that humans are not needed to label the data!
This type of model develops a statistical understanding of the language it has been trained on, but it’s not very useful for specific practical tasks. Because of this, the general pretrained model then goes through a process called transfer learning. During this process, the model is fine-tuned in a supervised way — that is, using human-annotated labels — on a given task
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https://t.me/DataScienceT
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⭐️ Python Courses:
https://t.me/Python53Speech to Text using Python
✅ More ♥️♥️ = more posts
@CodeProgrammer ♥️
The Data Science and Python channel is for researchers and advanced programmers
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