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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 68 482 suscriptores, ocupando la posición 2 406 en la categoría Educación y el puesto 4 586 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 482 suscriptores.

Según los últimos datos del 04 noviembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -75, y en las últimas 24 horas de 7, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.38%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.57% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 2 314 visualizaciones. En el primer día suele acumular 1 073 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 9.
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 05 noviembre, 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.

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68 482

Suscriptores

+724 horas

-597 días

-7530 días

2 314

Visitas de la publicación

~ 1 07324 horas

~ 1 59648 horas

3.38%

Tasa de compromiso

~ 2

Mensajes por día

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Archivo de publicaciones

68 482

https://t.me/+MhmkscCzIYQ2MmM8 Daily books

68 482

Our page in Facebook https://www.facebook.com/DataScience9 Please subscribe to get paid content

68 482

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68 482

🦙 Code Llama The most powerful AI assistant for writing Python code. • Github: https://github.com/facebookresearch/codellama • Docs: https://ai.meta.com/blog/code-llama-large-language-model-coding/ • Post: https://ai.meta.com/blog/code-llama-large-language-model-coding/ https://t.me/CodeProgrammer

68 482

Is your life too monotonous? Tired of waking up at 7am every day and driving through traffic to work, while getting a small salary? If you're watching this video, you've probably already thought about the fact that you want to change your life and earn good money without even leaving home! Well, I have a solution for you, consider yourself insanely lucky! Right now go to the link below, subscribe to our telegram channel, you and everyone who watches this video, waiting for a guaranteed income! Stop waiting, start changing your life right now! 👉 https://t.me/+EDfZMgnz5LNlYzlh 👉 https://t.me/+EDfZMgnz5LNlYzlh

68 482

👁‍🗨 Running YOLOv7 algorithm on your webcam using Ikomia API

from ikomia.dataprocess.workflow import Workflow
from ikomia.utils import ik
from ikomia.utils.displayIO import display
import cv2

stream = cv2.VideoCapture(0)

# Init the workflow
wf = Workflow()

# Add color conversion
cvt = wf.add_task(ik.ocv_color_conversion(code=str(cv2.COLOR_BGR2RGB)), auto_connect=True)

# Add YOLOv7 detection
yolo = wf.add_task(ik.infer_yolo_v7(conf_thres="0.7"), auto_connect=True)

while True:
    ret, frame = stream.read()
    
    # Test if streaming is OK
    if not ret:
        continue

    # Run workflow on image
    wf.run_on(frame)

    # Display results from "yolo"
    display(
        yolo.get_image_with_graphics(),
        title="Object Detection - press 'q' to quit",
        viewer="opencv"
    )

    # Press 'q' to quit the streaming process
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# After the loop release the stream object
stream.release()

# Destroy all windows
cv2.destroyAllWindows()

68 482

👁‍🗨 Running YOLOv7 algorithm on your webcam using Ikomia API

68 482

Join us for an exhilarating Python Singula Meetup Online! 🌐🎉 We bring together Python enthusiasts, developers, and learners from around the world! 🗓️ Date: August 29 ⏰Time: 6:00pm CET 🔗 To sign up 📺 The meetup will be broadcasted via YouTube! Our lineup of esteemed speakers will dive into exciting topics: ▶Discover the reasons behind Python's occasional slowness in specific tasks ▶Learn practical strategies to secure a Hadoop cluster within a large ML team ▶Unleash the power of spatial data with expert feature engineering 📌Subscribe to our Telegram channel, where you can find all the latest announcements for upcoming meetups!

68 482

Introduction to Python

Learn fundamental concepts for Python beginners that will help you get started on your journey to learn Python. These tutorials focus on the absolutely essential things you need to know about Python.

What You’ll Learn: • Installing a Python environment • The basics of the Python language https://realpython.com/learning-paths/python3-introduction/ https://t.me/CodeProgrammer

68 482

🔭 Daily Useful Scripts Daily.py is a repository that provides a collection of ready-to-use Python scripts for automating common daily tasks. git clone https://github.com/Chamepp/Daily.py.git ▪ Github: https://github.com/Chamepp/Daily.py https://t.me/CodeProgrammer

68 482

🖥 5 useful Python automation scripts 1. Download Youtube videos pip install pytube

from pytube import YouTube

# Specify the URL of the YouTube video
video_url = "https://www.youtube.com/watch?v=dQw4w9WgXcQ"

# Create a YouTube object
yt = YouTube(video_url)

# Select the highest resolution stream
stream = yt.streams.get_highest_resolution()

# Define the output path for the downloaded video
output_path = "path/to/output/directory/"

# Download the video
stream.download(output_path)

print("Video downloaded successfully!")

2. Automate WhatsApp messages pip install pywhatkit

import pywhatkit

# Set the target phone number (with country code) and the message
phone_number = "+1234567890"
message = "Hello, this is an automated WhatsApp message!"

# Schedule the message to be sent at a specific time (24-hour format)
hour = 13
minute = 30

# Send the scheduled message
pywhatkit.sendwhatmsg(phone_number, message, hour, minute)

3. Google search with Python


pip install googlesearch-python

from googlesearch import search

# Define the query you want to search
query = "Python programming"

# Specify the number of search results you want to retrieve
num_results = 5

# Perform the search and retrieve the results
search_results = search(query, num_results=num_results, lang='en')

# Print the search results
for result in search_results:
    print(result)

4. Download Instagram posts pip install instaloader

import instaloader

# Create an instance of Instaloader
loader = instaloader.Instaloader()

# Define the target Instagram profile
target_profile = "instagram"

# Download posts from the profile
loader.download_profile(target_profile, profile_pic=False, fast_update=True)

print("Posts downloaded successfully!")

5. Extract audio from video files pip install moviepy


from moviepy.editor import VideoFileClip

# Define the path to the video file
video_path = "path/to/video/file.mp4"

# Create a VideoFileClip object
video_clip = VideoFileClip(video_path)

# Extract the audio from the video
audio_clip = video_clip.audio

# Define the output audio file path
output_audio_path = "path/to/output/audio/file.mp3"

# Write the audio to the output file
audio_clip.write_audiofile(output_audio_path)

# Close the clips
video_clip.close()
audio_clip.close()

print("Audio extracted successfully!")

https://t.me/CodeProgrammer

68 482

Best-of Python 🏆 A ranked list of awesome Python open-source libraries & tools. Updated weekly. ▪ Github: https://github.com/ml-tooling/best-of-python https://t.me/CodeProgrammer More reaction. please ⭐️💐⭐️

68 482

🖥 Text-to-Speech with PyTorch

import torchaudio
import torch
import matplotlib.pyplot as plt
import IPython.display

bundle = torchaudio.pipelines.TACOTRON2_WAVERNN_PHONE_LJSPEECH

processor = bundle.get_text_processor()
tacotron2 = bundle.get_tacotron2().to(device)  # Move model to the desired device
vocoder = bundle.get_vocoder().to(device)      # Move model to the desired device

text = " My first text to speech!"

with torch.inference_mode():
    processed, lengths = processor(text)
    processed = processed.to(device)      # Move processed text data to the device
    lengths = lengths.to(device)          # Move lengths data to the device
    spec, spec_lengths, _ = tacotron2.infer(processed, lengths)
    waveforms, lengths = vocoder(spec, spec_lengths)

    
fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(16, 9))
ax1.imshow(spec[0].cpu().detach(), origin="lower", aspect="auto")  # Display the generated spectrogram
ax2.plot(waveforms[0].cpu().detach())                             # Display the generated waveform7. Play the generated audio using IPython.display.Audio
IPython.display.Audio(waveforms[0:1].cpu(), rate=vocoder.sample_rate)

https://t.me/CodeProgrammer

68 482

🖥 Text-to-Speech with PyTorch https://t.me/CodeProgrammer

68 482

Your First Deep Learning Project in Python with Keras Step-by-Step https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/ https://t.me/CodeProgrammer More reaction please ⭐️💐⭐️

68 482

👱‍♂️ Creating Face Swaps with Python and OpenCV Step 1: Face Detection

import cv2
def detect_face(image_path):
    # Load the face detection classifier
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

    # Read and convert the image to grayscale
    image = cv2.imread(image_path)
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Detect faces in the image
    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5)

    # Assuming there's only one face in the image, return its coordinates
    if len(faces) == 1:
        return faces[0]
    else:
        return None

Step 2: Swapping Faces

def main():
    # Paths to the input images
    image_path_1 = 'path_to_image1.jpg'
    image_path_2 = 'path_to_image2.jpg'

    # Detect the face in the second image
    face_coords_2 = detect_face(image_path_2)
    if face_coords_2 is None:
        print("No face found in the second image.")
        return

    # Load and resize the source face
    image_1 = cv2.imread(image_path_1)
    face_width, face_height = face_coords_2[2], face_coords_2[3]
    image_1_resized = cv2.resize(image_1, (face_width, face_height))

    # Extract the target face region from the second image
    image_2 = cv2.imread(image_path_2)
    roi = image_2[face_coords_2[1]:face_coords_2[1] + face_height, face_coords_2[0]:face_coords_2[0] + face_width]

    # Flip the target face horizontally
    reflected_roi = cv2.flip(roi, 1)

    # Blend the two faces together
    alpha = 0.7
    blended_image = cv2.addWeighted(image_1_resized, alpha, reflected_roi, 1 - alpha, 0)

    # Replace the target face region with the blended image
    image_2[face_coords_2[1]:face_coords_2[1] + face_height, face_coords_2[0]:face_coords_2[0] + face_width] = blended_image

    # Display the result
    cv2.imshow('Blended Image', image_2)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

if name == "main":
    main()

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68 482

👱‍♂️ Creating Face Swaps with Python and OpenCV https://t.me/CodeProgrammer More reaction please ⭐️💐⭐️

68 482

How to Train an Object Detection Model with Keras https://machinelearningmastery.com/how-to-train-an-object-detection-model-with-keras/ https://t.me/CodeProgrammer More reaction please ⭐️💐⭐️