Python | Machine Learning | Coding

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

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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/

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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

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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

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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
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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

Python | Machine Learning | Coding | R

🖥 Text-to-Speech with PyTorch

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5.8K views15:16

Python | Machine Learning | Coding | R

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

🖥 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)

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6.1K viewsedited 15:17

Python | Machine Learning | Coding | R

Best-of Python

🏆 A ranked list of awesome Python open-source libraries & tools. Updated weekly.

▪ Github: https://github.com/ml-tooling/best-of-python

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6.7K viewsedited 15:33

Python | Machine Learning | Coding | R

🖥 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!")

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6.9K views09:27

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