متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
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
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Machine Learning with Python
تُعد قناة Machine Learning with Python (@codeprogrammer) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 67 813 مشتركاً، محتلاً المرتبة 2 427 في فئة التعليم والمرتبة 5 028 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 67 813 مشتركاً.
بحسب آخر البيانات بتاريخ 13 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 60، وفي آخر 24 ساعة بمقدار -3، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 4.31%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.69% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 2 926 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 148 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 6.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل insidead, learning, degree, evaluation, algorithm.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 14 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.
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🖥 GitHub
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🟢 Yaoliang Yu, a professor at the School of Computer Science at the University of Waterloo, Canada, has published several free data science courses. These courses include machine learning, data science optimization, linear algebra and deep learning.
✅ The resources of each course include textbooks, assignments, articles and projects during the course.
🔖 Guide to free data science courses at the University of Waterloo:
┌ ➡️ CS794 Fall 2022
└ 🖥 Optimization for Data Science
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└ 🖥 Introduction to Machine Learning
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In 1989, Yann LeCun and his team trained a LeNet 1 CNN, which was able to detect handwriting.
They published a video showing how this model can read the numbers that were written manually on a piece of paper, and then the model gives the numbers electronically.
The Convolution Neural Network CNN algorithm is considered one of the algorithms that has influenced the world and we find it nowadays in many fields.
In general, everything that can be predicted from an image or video is a CNN.
Many researchers relied on this algorithm and derived many of the most famous models from it (ResNet, DenseNet, MobileNet, SqueezeNet, VGG)
There are many models that come under the name CNN
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🔺 The best GitHub repositories for learning Python
✅ Learn Python for Data Science in 2024
👨🏻💻 In the latest data science report 2024 , Python is still the top programming language for data science with 56.7% . Here I have put a list of the best Python repositories for data science , which will improve your coding skills and guide you on the path to data science mastery.💯
1️⃣ Learn Python 3 repo
🖥 A collection of Jupyter notebooks for learning Python.
🐱 GitHub repo link
2️⃣ The Algorithms repo
🖥 All algorithms implemented in Python for training.
🐱 GitHub repo link
3️⃣ Awesome Python repo
🖥 A list of great Python frameworks, libraries, software, and resources.
🐱 GitHub repo link
4️⃣ 100 Days of ML repo
🖥 Learning algorithms and building neural networks without any programming experience.
🐱 GitHub repo link
5️⃣ Cosmic Python book repo
🖥 A book on Python's functional architectural patterns for managing complexity.
🐱 GitHub repo link
6️⃣ A Byte of Python book repo
🖥 If you do not learn Python programming, start with this book.
🐱 GitHub repo link
7️⃣ Python Machine Learning book repo
🖥 Python Machine Learning book code repository.
🐱 GitHub repo link
8️⃣ Repo of interactive interview challenges
🖥 120+ interactive Python coding interview challenges.
🐱 GitHub repo link
9️⃣ Repo of coding problems
🖥 Solutions for various coding/algorithmic problems.
🐱 GitHub repo link
1️⃣ Python Basics repo
🖥 A list of 300 Python interview questions + answer sheet.
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1️⃣ Python programming exercises repo
🖥 100+ challenging Python programming exercises.
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⚡️ Graph Machine Learning
Free advanced course: Machine learning on graphs .
The course is regularly supplemented with practical problems and slides. The author Xavier Bresson is a professor at the National University of Singapore.
▪ Introduction
▪ Dive into graphs
- Lab1: Generate LFR social networks
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code01.ipynb
- Lab2: Visualize spectrum of point cloud & grid
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code02.ipynb
- Lab3/4: Graph construction for two-moon & text documents
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code03.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code04.ipynb
▪ Graph clustering
- Lab1: k-means
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code01.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code02.ipynb
- Lab2: Metis
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code03.ipynb
- Lab3/4: NCut/PCut
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code04.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code05.ipynb
- Lab5: Louvain
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code06.ipynb
https://pic.twitter.com/vSXCx364pe
▪ Lectures 4 Graph SVM
- Lab1 : Standard/Linear SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code01.ipynb
- Lab2 : Soft-Margin SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code02.ipynb
- Lab3 : Kernel/Non-Linear SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code03.ipynb
- Lab4 : Graph SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code04.ipynb
Running instructions: https://storage.googleapis.com/xavierbresson/lectures/CS6208/running_notebooks.pdf
💡 Github
✅ https://t.me/DataScienceT
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