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

Network:Machine Learning with Python India5 035 Education2 428...

📈 Analytical overview of Telegram channel Machine Learning with Python

Channel Machine Learning with Python (@codeprogrammer) in the English language segment is an active participant. Currently, the community unites 67 833 subscribers, ranking 2 428 in the Education category and 5 035 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 833 subscribers.

According to the latest data from 15 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 82 over the last 30 days and by 13 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 4.40%. Within the first 24 hours after publication, content typically collects 1.74% reactions from the total number of subscribers.
Post reach: On average, each post receives 2 983 views. Within the first day, a publication typically gains 1 177 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 16 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

67 833

Subscribers

+1324 hours

+187 days

+8230 days

2 983

Post views

~ 1 17724 hours

~ 1 70948 hours

4.40%

Engagement rate

~ 4

Posts per day

Ads index

beta

Posts Archive

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https://t.me/DataScienceT surprise

67 833

Download Data Science free Courses https://t.me/udemy13

67 833

القناة دى قمة فى الروعة في البرمجة وفيها حوالى 40 دورة انصحكوا تشتركوا فيها 👏💙💞 https://www.youtube.com/channel/UCGbrg29FWhK503HN0KsPkjA?sub_confirmation=1 ودا جروب تليجرام تقدر تحصل فيه كورسات برمجية فى اى مجال حرفيا https://t.me/CISArab لو انت متخصص فى تراك ال PHP Laravel دا جروب رائع https://t.me/phpdevelopers2024 اما لو متخصص فى ال .Net Core فدا جروب عليه مشاريع كبيرة جدا https://t.me/C_Sharp_Developers اما لو بتحب البايثون https://t.me/learncsharp_programing

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

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

67 833

Our page in Facebook https://www.facebook.com/DataScience9 Please subscribe

67 833

🦙 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

67 833

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

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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()

67 833

👁‍🗨 Running YOLOv7 algorithm on your webcam using Ikomia API

67 833

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!

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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

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🔭 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

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🖥 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

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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 ⭐️💐⭐️

67 833

🖥 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

67 833

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

67 833

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 ⭐️💐⭐️