Python | Machine Learning | Coding

Forwarded from Eng. Hussein Sheikho 👨‍💻

with AFT, Get Forex Trading free signals we will guide you to earn and make money in crypto and forex!

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7.5K views13:33

Python | Machine Learning | Coding | R

🖥 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

9.2K views14:06

Python | Machine Learning | Coding | R

35 Best+FREE Coursera Courses for Data Science and Machine Learning!
https://www.mltut.com/best-coursera-courses-for-data-science/

https://t.me/CodeProgrammer

10.7K viewsedited 13:51

Python | Machine Learning | Coding | R

carbon - 2023-08-08T115041.994.png

1.3 MB

🖐 Python Mouse Control Remotely With Your Hand.

▪ Source Code: https://gist.github.com/Develp10/3d605ce6ef017fdfc3e66e147ec9cc18

https://t.me/CodeProgrammer

8.6K viewsedited 16:11

Python | Machine Learning | Coding | R

🖥 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

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7.4K viewsedited 16:08

Python | Machine Learning | Coding | R

👨‍🎓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/

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7.3K viewsedited 09:46

Python | Machine Learning | Coding | R

📚 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
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8.1K viewsedited 19:50

Python | Machine Learning | Coding | R

⚡ 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/

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8.4K viewsedited 18:22

Python | Machine Learning | Coding | R

Daily python books
https://t.me/DataScience4

Daily python books
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6.3K viewsedited 06:14

Python | Machine Learning | Coding | R

How to Train an Object Detection Model with Keras

https://machinelearningmastery.com/how-to-train-an-object-detection-model-with-keras/

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6.7K viewsedited 06:21

Python | Machine Learning | Coding | R

👱‍♂️ Creating Face Swaps with Python and OpenCV

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6.2K viewsedited 15:35

Python | Machine Learning | Coding | R

👱‍♂️ 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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7.5K viewsedited 15:36

Python | Machine Learning | Coding | R

Your First Deep Learning Project in Python with Keras Step-by-Step

https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/

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6.2K viewsedited 11:50

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