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لا تقع ضحية للمخادعين! تيليمتريو يكتشف ويُميّز هذه القنوات 👉 إذا كنت تريد رؤية العلامة، اشترك

Machine Learning with Python

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 68 471 مشتركاً، محتلاً المرتبة 2 400 في فئة التعليم والمرتبة 4 598 في منطقة الهند.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 68 471 مشتركاً.

بحسب آخر البيانات بتاريخ 31 أكتوبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 12، وفي آخر 24 ساعة بمقدار -24، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 3.99‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.46‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 2 735 مشاهدة. وخلال اليوم الأول يجمع عادةً 998 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 8.
  • 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 01 نوفمبر, 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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Awesome interactive textbook on probability theory and statistics Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works Get it here, I recommend opening it on a desktop https://seeing-theory.brown.edu/ 👉 @DataScienceM

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Awesome interactive textbook on probability theory and statistics Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works Get it here, I recommend opening it on a desktop https://seeing-theory.brown.edu/ 👉 @DataScienceM

Repost from Machine Learning
Awesome interactive textbook on probability theory and statistics Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works Get it here, I recommend opening it on a desktop https://seeing-theory.brown.edu/ 👉 @DataScienceM I spent years chasing success until I found the 7 daily habits no one talks about—now everything’s changed for me. Most people miss the real secret. See what you’ve been overlooking: Success Tips 🔥 | InsideAds

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📌 How to Build a Genetic Algorithm from Scratch in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-30 | ⏱️ Read time: 16 m
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Python library RetinaFace for face detection and working with key points (eyes, nose, mouth) Supports face alignment, easily
Python library RetinaFace for face detection and working with key points (eyes, nose, mouth) Supports face alignment, easily installed via pip install retina-face, and works based on deep models from the insightface project. An excellent tool for tasks in computer vision and face recognition. Usage examples:
from retinaface import RetinaFace

resp = RetinaFace.detect_faces("img1.jpg")
print(resp)

{
    "face_1": {
        "score": 0.9993440508842468,
        "facial_area": [155, 81, 434, 443],
        "landmarks": {
          "right_eye": [257.82974, 209.64787],
          "left_eye": [374.93427, 251.78687],
          "nose": [303.4773, 299.91144],
          "mouth_right": [228.37329, 338.73193],
          "mouth_left": [320.21982, 374.58798]
        }
  }
}
👉 @DataScienceN

Creating QR codes with Python in just a few lines of code Anyone can generate their own QR code for a link, text, or even Wi-
Creating QR codes with Python in just a few lines of code Anyone can generate their own QR code for a link, text, or even Wi-Fi data. For this, the qrcode library and the PIL module are used
pip install qrcode pillow
import qrcode
from PIL import Image

data = input("Enter data for QR: ")
qr = qrcode.QRCode(version=3, box_size=8, border=4)
qr.add_data(data)
qr.make(fit=True)

image = qr.make_image(fill="black", back_color="aqua")
image.save("qr_code.png")
Image.open("qr_code.png")
The output is a ready QR code with any text or link. You can change colors, sizes, and style to fit your design 🙂 👉 https://t.me/CodeProgrammer

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📌 A Guide to Clustering Algorithms 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-06 | ⏱️ Read time: 6 min read An overview of c
📌 A Guide to Clustering Algorithms 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-06 | ⏱️ Read time: 6 min read An overview of clustering and the different families of clustering algorithms.

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📌 Image Segmentation With K-Means Clustering 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-05 | ⏱️ Read time: 11 min read A
📌 Image Segmentation With K-Means Clustering 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-05 | ⏱️ Read time: 11 min read An introduction with Python

Google Collab notebooks to learn everything you need to master prompt engineering with Claude - from basic structure and role
Google Collab notebooks to learn everything you need to master prompt engineering with Claude - from basic structure and role prompting to advanced techniques like few-shot learning, avoiding hallucinations, and tool use. Perfect interactive lessons to level up your AI skills Link: https://github.com/anthropics/courses/tree/master/prompt_engineering_interactive_tutorial/Anthropic%201P

Soon 😉

Think trading is all about luck or chasing fast money? The truth is much simpler: real results come from steady discipline, s
Think trading is all about luck or chasing fast money? The truth is much simpler: real results come from steady discipline, smart risk management, and total transparency. Every day, we post real trading results—no hype, just facts. Want to see what’s really possible? Discover today’s actual performance right here. Get honest insight—join Elite Gold Trading! #ad InsideAds

Our paid channel contains more than 9000 books and 500 paid courses With a $3 monthly subscription, you can cancel whenever you want https://t.me/+r_Tcx2c-oVU1OWNi

Harvard's "Advanced Algorithms" by Jelani Nelson 📽 Lecture Videos: https://youtube.com/playlist?list=PL2SOU6wwxB0uP4rJgf5ayh
Harvard's "Advanced Algorithms" by Jelani Nelson 📽 Lecture Videos: https://youtube.com/playlist?list=PL2SOU6wwxB0uP4rJgf5ayhHWgw7akUWSf 📷 Lecture Notes: https://people.seas.harvard.edu/~cs224/fall14/lec.html

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“Nobody believed I could double my deposit safely… But inside Elite Gold Trading, we proved it: real people, real profits, re
“Nobody believed I could double my deposit safely… But inside Elite Gold Trading, we proved it: real people, real profits, real discipline. Want to see how our members avoid big losses and keep winning? 👉 Discover the secret inside #ad InsideAds

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📌 A Basic Introduction to Quantum GANs 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-21 | ⏱️ Read time: 11 min read “Quantu
📌 A Basic Introduction to Quantum GANs 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-21 | ⏱️ Read time: 11 min read “Quantum computing just becomes vastly simpler once you take the physics out of it.”