Available now! Telegram Research 2025 — the year's key insights 
Machine Learning with Python
Open in Telegram
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
Show more📈 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
Posts Archive
👱♂️ 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
More reaction please ⭐️💐⭐️
+1👱♂️ Creating Face Swaps with Python and OpenCV
https://t.me/CodeProgrammer
More reaction please ⭐️💐⭐️
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 ⭐️💐⭐️
Daily python books
https://t.me/DataScience4
Daily python books
https://t.me/DataScience4
⚡ Top 100+ Machine Learning Projects for 2023 [with Source Code]
In this article, you will find 100+ of the best machine learning projects and ideas that will be useful for both beginners and experienced professionals.
📌Projects: https://www.geeksforgeeks.org/machine-learning-projects/
https://t.me/CodeProgrammer
More reaction please ⭐️💐⭐️
📚 9 must-have Python developer tools.
1. PyCharm IDE
2. Jupyter notebook
3. Keras
4. Pip Package
5. Python Anywhere
6. Scikit-Learn
7. Sphinx
8. Selenium
9. Sublime Text
https://t.me/CodeProgrammer
More reaction please ⭐️💐⭐️
👨🎓Harvard CS50’s Artificial Intelligence with Python – Full University Course
This free course from Harvard University explores the concepts and algorithms behind modern artificial intelligence.
🎞 Video: https://www.youtube.com/watch?v=5NgNicANyqM
📌 Course resources: https://cs50.harvard.edu/ai/2020/
https://t.me/CodeProgrammer
More reaction please 👌
🖥 Importing Data from SQL Server to Excel with Multiple Sheets using Python
📝 Source Code: https://github.com/danis111/Importing-Data-from-SQL-Server-to-Excel-with-Multiple-Sheets-using-Python/tree/main
https://t.me/CodeProgrammer
More reaction please 👌
🖐 Python Mouse Control Remotely With Your Hand.
▪ Source Code: https://gist.github.com/Develp10/3d605ce6ef017fdfc3e66e147ec9cc18
https://t.me/CodeProgrammer
This channels is for Programmers, Coders, Software Engineers.
0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning
8- programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/DataScienceM
35 Best+FREE Coursera Courses for Data Science and Machine Learning!
https://www.mltut.com/best-coursera-courses-for-data-science/
Ты хочешь рекламировать известные бренды и зарабатывать на этом?
Скачивай приложение Perfluence на свой телефон по ссылке
для регистрации нужно указать ключ. Выбирай офферы для размещения в своих соцсетях, делай публикации и зарабатывай с Perfluence 🤩
Все крутые бренды в одном месте, быстрая регистрация и выгодная монетизация ждут вас в приложении Perfluence 😉
|@perfluence_net
#perfluence
+7🖥 8 delightful Python scripts that will brighten your day
8 cool python scripts to brighten up your day .
These little gems will add some fun to your programming projects.
1. Speed test
2. Convert photo to cartoon format
3. Site status output
4. Image enhancement
5. Creating a web bot
6. Conversion: Hex to RGB
7. Convert PDF to images
8. Get song lyrics
https://t.me/CodeProgrammer
with AFT, Get Forex Trading free signals we will guide you to earn and make money in crypto and forex!
https://t.me/tradingaft
Repost from Machine Learning
Do you want to do advertising promotion in a way that ensures that you pay for the number of real people who join your channel?
You will only pay an amount depending on the number of people who have joined
https://adsly.me/register?advertiser&ref=1108
Through this platform, you can launch advertising campaigns, and this platform guarantees your right that your ads not be in vain
Adsly Platform: https://adsly.me/register?advertiser&ref=1108
BTP CRYPTO PUMPS & SIGNALS
We offer short crypto pumps & signals 28-30 times per month.
#ad
BTP CRYPTO PUMPS & SIGNALS
We offer short crypto pumps & signals 28-30 times per month.
#ad
