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

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/

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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

Python | Machine Learning | Coding | R

🔭 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

6.9K views15:24

Python | Machine Learning | Coding | R

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/

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7.9K viewsedited 05:14

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