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
الشبكة:📈 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 68 102 مشتركاً، محتلاً المرتبة 2 372 في فئة التعليم والمرتبة 4 808 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 68 102 مشتركاً.
بحسب آخر البيانات بتاريخ 27 أغسطس, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 112، وفي آخر 24 ساعة بمقدار 8، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 4.52%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.90% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 3 077 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 291 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 5.
- 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 28 أغسطس, 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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Machine Learning in Python (Course Notes)
I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you!
Here’s what you’ll learn:
🔘 Linear Regression - The foundation of predictive modeling
🔘 Logistic Regression - Predicting probabilities and classifications
🔘 Clustering (K-Means, Hierarchical) - Making sense of unstructured data
🔘 Overfitting vs. Underfitting - The balancing act every ML engineer must master
🔘 OLS, R-squared, F-test - Key metrics to evaluate your models
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Comment
Посилання на сайт: https://applicationhubs.com/?utm_source=telegram&utm_campaign=collaborator зробити кнопкою з текстом "Спробувати ApplicationHubs"
Publication text
Знайшов цікавий сервіс для розробників — ApplicationHubs.
Це платформа, яка дозволяє запускати повноцінне Linux-середовище розробки у хмарі. Можна створити свій Dev Hub, підключитися через SSH, VSCode Remote або JetBrains Gateway і працювати як на звичайному комп'ютері — тільки без налаштування локального середовища.
Підтримуються Docker-проєкти, будь-які мови та фреймворки.
По суті це персональна cloud development machine, яку можна запустити за кілька секунд.
Зараз відкрито ранній доступ (early access).
[👉 https://applicationhubs.com](https://applicationhubs.com/?utm_source=telegram&utm_campaign=collaborator)
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Repost from Learn Python Coding
The Ultimate 2026 Python Learning Roadmap: From Beginner to Expert
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Repost from Learn Python Coding
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
🧠 Python libraries for AI agents - complexity of learning 🔥
🟢 Easy
• LangChain
• tool calling
• agent memory
• simple agents
• CrewAI
• agents with roles
• collaboration of several agents
• SmolAgents
• lightweight agents
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🟡 Medium
• LangGraph
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• agent orchestration
• LlamaIndex
• RAG pipelines
• data indexing
• knowledge agents
• OpenAI Agents SDK
• tool integrations
• agent workflows
• Strands
• agent orchestration
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• Semantic Kernel
• skills / plugins
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• PydanticAI
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• Langroid
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🔴 Difficult
• AutoGen
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• DSPy
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• optimization of LLM pipelines
• A2A
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https://t.me/CodeProgrammer ✅
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Pandas cheat sheet
Use the following Pandas cheat sheet to quickly reference some of the most common operations you might perform with the Pandas library.
More: https://www.coursera.org/resources/pandas-cheat-sheet
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Repost from Learn Python Coding
Python Cheat Sheet: Beginner to Expert Guide
This #Python cheat sheet covers basics to advanced concepts, regex, list slicing, loops and more. Perfect for quick reference and enhancing your coding skills.
Read: https://www.almabetter.com/bytes/cheat-sheet/python
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Matplotlib Cheat Sheet (Basics to Advanced)
Learn key Matplotlib functions with our Matplotlib cheat sheet. Includes examples, advanced customizations and comparison with Seaborn for better visualizations
Matplotlib is a versatile library in Python used for data visualization. Matplotlib enables the creation of static, interactive, and animated visualizations in Python. It is highly customizable and integrates well with libraries like Pandas and NumPy. Its pyplot module simplifies the process of creating plots similar to MATLAB. This Matplotlib cheat sheet provides an overview of the essential functions, features, and tools available in Matplotlib, along with comparisons to Seaborn where relevant.
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