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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📈 Análisis del canal de Telegram Machine Learning with Python

El canal Machine Learning with Python (@codeprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 67 818 suscriptores, ocupando la posición 2 429 en la categoría Educación y el puesto 5 036 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 818 suscriptores.

Según los últimos datos del 14 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 66, y en las últimas 24 horas de 5, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.52%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.70% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 3 064 visualizaciones. En el primer día suele acumular 1 155 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
Intereses temáticos: El contenido se centra en temas clave como insidead, learning, degree, evaluation, algorithm.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 15 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.

67 818

Suscriptores

+524 horas

Sin datos7 días

+6630 días

3 064

Visitas de la publicación

~ 1 15524 horas

~ 1 68748 horas

4.52%

Tasa de compromiso

~ 4

Mensajes por día

Ads index

beta

Archivo de publicaciones

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Get and download all Udemy courses and other courses for only $10 Create an account on Patreon patreon.com/DataScienceBooks

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CNN datasheet https://t.me/Hussein_Sheikho/s/19

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python

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import PyPDF2
import pyttsx3
# Read the pdf by specifying the path in your computer 
pdfReader = PyPDF2.PdfFileReader(open('clcoding.pdf', 'rb'))
# Get the handle to speaker
speaker = pyttsx3.init()
# split the pages and read one by one
for page_num in range(pdfReader.numPages): 
    text = pdfReader.getPage(page_num).extractText() 
    speaker.say(text)
    speaker.runAndWait()
# stop the speaker after completion 
speaker.stop() 
# save the audiobook at specified path 
engine.save_to_file(text, 'E:\audio.mp3') 
engine.runAndWait()

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🔅 Create an Audiobook in Python

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❤️‍🔥Hacking the WIFI password❤️‍🔥 ✅ By: https://t.me/Python53

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🌴Machine Learning A-Z™: AI, Python & R + ChatGPT Bonus [2023] 🌴 Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included. This course includes: 42 hours on-demand video 5 coding exercises 39 articles 9 downloadable resources 🟢 Price :- $20 Source :- https://www.udemy.com/course/machinelearning/ Contact @hussein_sheikho

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🖥 Face swap on python.

pip install --user opencv-python insightface matplotlib # скачай 'inswapper_128.onnx' отсюда 'clck.ru/36ct6v' и закинь в папку с кодом import cv2 import insightface from insightface.app import FaceAnalysis import matplotlib.pyplot as plt app = FaceAnalysis(name='buffalo_l') app.prepare(ctx_id=0, det_size=(640, 640)) swapper = insightface.model_zoo.get_model('inswapper_128.onnx') def swap_faces(img): img = cv2.imread(img) fig, ax = plt.subplots(1, 1, figsize=(10, 5)) ax.imshow(img[:,:,::-1]) ax.axis('off'); plt.show() faces = app.get(img) face1, face2 = faces[0], faces[1] imgnew = img.copy() imgnew = swapper.get(imgnew, face1, face2, paste_back=True) imgnew = swapper.get(imgnew, face2, face1, paste_back=True) fig, ax = plt.subplots(1, 1, figsize=(10, 5)) ax.imshow(imgnew[:,:,::-1]) ax.axis('off'); plt.show() return imgnew swap_faces('ТУТ ТВОЁ ИЗОБРАЖЕНИЕ')

🎥 Video: https://www.youtube.com/shorts/78CFNgTTX54

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Soon Our new channel - Datasets repositories https://t.me/datasets1

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Repost from Data Science Books

Ultimate Python Bootcamp For Data Science & Machine Learning Learn How To Code Python For Data Science, ML & Data Analysis, With 100+ Exercises and 4 Real Life Projects ! Udemy Link : https://www.udemy.com/course/data-analysis-with-pandas-a-complete-tutorial/ Download full course below 👇 https://t.me/+LnCmAFJO3tNmYjUy Price 7$ for all courses Contact @Hussein_Sheikho

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Soon Our new channel - Datasets repositories https://t.me/datasets1

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Soon Our new channel - Datasets repositories https://t.me/datasets1

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