متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
Machine Learning with Python
الذهاب إلى القناة على Telegram
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 2 417 في فئة التعليم والمرتبة 5 033 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 67 813 مشتركاً.
بحسب آخر البيانات بتاريخ 11 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 56، وفي آخر 24 ساعة بمقدار 0، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 3.96%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 2.43% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 2 683 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 650 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 12 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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Best LLMs Courses
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#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents
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Master #DataAnalytics for real-world problems. I have curated a list of 40 Data Analytics Projects (solved & explained) that will help you build analytics skills using #Python.
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1. Rainfall Trends in India Analysis
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3. Creating a Mutual Fund Plan
4. Stock Market Portfolio Optimization
5. Metro Operations Optimization
6. Analyzing the Impact of Carbon Emissions
Find this list of projects here:
https://thecleverprogrammer.com/2024/11/01/data-analytics-projects-with-python/
📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #deeplearning #Projects
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▶️ The best project oriented videos
💻 To learn data science
👨🏻💻 The best way to learn data science is to combine theoretical learning with practical projects. After you have mastered the main concepts and tools, stop wasting your time and start learning project-based and doing real projects!
✅ Here I put 17 real data science projects in video form:👇
1️⃣ Making machine learning transparent with LINK ⬅️ AI
2️⃣ LINK practical guide ⬅️ PySpark
3️⃣ Construction of LINK model ⬅️ SRKGPT
4️⃣ Building a recovery system ⬅️ LINK
5️⃣ Strengthening small models ⬅️ (2) , (1)
6️⃣ Set Falon and Lama-2 ⬅️ (2) , (1)
7️⃣ Embedding data using LINK ⬅️ BERT
8️⃣ Compression of models ⬅️ LINK
9️⃣ Dynamic pricing in business ⬅️ LINK
1️⃣ Product embedding in big data ⬅️ LINK
1️⃣ Twitter recommender systems ⬅️ LINK
1️⃣ How the Twitter algorithm works ⬅️ LINK
1️⃣ Pinterest advertising systems ⬅️ LINK
1️⃣ Search for the nearest neighbor ⬅️ LINK
1️⃣ LINK controller ⬅️ PID
1️⃣ Training neural networks ⬅️ LINK
⚫️ Data dimensionality reduction with LINK ⬅️ PCA
5 Free Courses for Mastering LLMs
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https://t.me/CodeProgrammer ✅
Pandas is getting outdated.
5 reasons you should move to FireDucks 👇
1. Requires changing ONLY ONE line of code:
↳ Replace "𝗶𝗺𝗽𝗼𝗿𝘁 𝗽𝗮𝗻𝗱𝗮𝘀 𝗮𝘀 𝗽𝗱" with "𝗶𝗺𝗽𝗼𝗿𝗲 𝗳𝗶𝗿𝗲𝗱𝘂𝗰𝗸𝘀.𝗽𝗮𝗻𝗱𝗮𝘀 𝗮𝘀 𝗽𝗱"
↳ The rest of the entire code remains the same.
↳ So, if you know Pandas, you already know how to use FireDucks.
↳ Done!
2. Ridiculously faster as per official benchmarks:
↳ Modin had an average speed-up of 0.9x over Pandas.
↳ Polars had an average speed-up of 39x over Pandas.
↳ But FireDucks had an average speed-up of 50x over Pandas.
3. Pandas is single-core; FireDucks is multi-core.
4. Pandas follows eager execution; FireDucks is based on lazy execution. This way, FireDucks can build a logical execution plan and apply possible optimizations.
5. That said, even under eager execution, FireDucks is way faster than Pandas, as depicted in the image below.
📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #FireDucks
http://t.me/codeprogrammer ⭐️
List of Running Processes using Python
📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Transformer
http://t.me/codeprogrammer ⭐️
🐱 7 of the best GitHub repos
✅ To enter the world of data analysis and data science
1⃣ 100 Days of ML Code repo
✍️ A hundred-day program for learning and practicing machine learning coding.
🔢 Awesome Data Science repo
✍️ A curated list of great data science resources such as books, software, and tools.
🔢 Data Science for Beginners repo
✍️ A repository from Microsoft that has a 10-week course with 20 lessons for beginners. Each lesson includes videos, quizzes, challenges and more.
🔢 Data Science Interviews Repo
✍️ A repository of questions and answers for science job interviews.
🔢 ML Technical Interviews repo
✍️ A good guide for machine learning and artificial intelligence technical interviews.
🔢 ML Interviews repo
✍️ A repository containing machine learning interview questions from basic topics to complex topics such as neural networks and reinforcement learning.
🔢 Data Science Python Notebooks repo
✍️ A collection of notebooks in various fields of data science such as deep learning, machine learning, data analysis and Python topics.
📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Transformer
http://t.me/codeprogrammer ⭐️
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Python List Methods clearly Explained
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Amazing premium resources only for my subscribers
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💠 All free Kaggle courses for data science
📁 Along with the course completion certificate
✅ Python ⬅️ link
✅ An introduction to machine learning ⬅️ link
✅ Pandas ⬅️ link
✅ Medium machine learning ⬅️ link
✅ Data visualization ⬅️ link
✅ Feature engineering ⬅️ link
✅ An introduction to the SQL language ⬅️ link
✅ Advanced SQL language ⬅️ link
✅ An introduction to deep learning ⬅️ link
✅ Computer vision ⬅️ link
✅ Time series ⬅️ link
✅ Data cleanup ⬅️ link
✅ Geographical analysis ⬅️ link
✅ Explainability of machine learning ⬅️ link
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