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Machine Learning with Python

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

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📈 Аналітичний огляд Telegram-каналу Machine Learning with Python

Канал Machine Learning with Python (@codeprogrammer) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 68 138 підписників, посідаючи 2 366 місце в категорії Освіта та 4 740 місце у регіоні Індія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 68 138 підписників.

За останніми даними від 30 серпня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 86, а за останні 24 години на 27, загальне охоплення залишається високим.

  • Статус верифікації: Не верифікований
  • Рівень залученості (ER): Середній показник залученості аудиторії становить 4.07%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.52% реакцій від загальної кількості підписників.
  • Охоплення публікацій: В середньому кожен допис отримує 2 775 переглядів. Протягом першої доби публікація в середньому набирає 1 037 переглядів.
  • Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 5.
  • Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як insidead, learning, degree, evaluation, algorithm.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

Завдяки високій частоті оновлень (останні дані отримано 31 серпня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.

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68 138
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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": {
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          "mouth_right": [228.37329, 338.73193],
          "mouth_left": [320.21982, 374.58798]
        }
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}
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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
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import qrcode
from PIL import Image

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

image = qr.make_image(fill="black", back_color="aqua")
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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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