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 68 482 підписників, посідаючи 2 406 місце в категорії Освіта та 4 586 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 68 482 підписників.
За останніми даними від 04 листопада, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -75, а за останні 24 години на 7, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 3.38%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.57% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 314 переглядів. Протягом першої доби публікація в середньому набирає 1 073 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 9.
- 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 05 листопада, 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.
68 482
Підписники
+724 години
-597 днів
-7530 днів
Архів дописів
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🦙 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
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👁🗨 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()
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
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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🔭 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🖥 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/CodeProgrammerBest-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
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🖥 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/CodeProgrammerYour First Deep Learning Project in Python with Keras Step-by-Step
https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/
https://t.me/CodeProgrammer
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👱♂️ 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()
https://t.me/CodeProgrammer
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https://t.me/CodeProgrammer
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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
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