Python | Machine Learning | Coding

Python | Machine Learning | Coding | R

Flask by Example

You’re going to start building a Flask app that calculates word-frequency pairs based on the text from a given URL. This is a full-stack tutorial covering a number of web development techniques. Jump right in and discover the basics of Python web development with the Flask microframework.

https://realpython.com/learning-paths/flask-by-example/

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7.2K viewsedited 06:08

Python | Machine Learning | Coding | R

👁‍🗨 Running YOLOv7 algorithm on your webcam using Ikomia API

6.9K views11:17

Python | Machine Learning | Coding | R

👁‍🗨 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()

https://t.me/CodeProgrammer

8.6K viewsedited 11:17

Python | Machine Learning | Coding | R

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🦙 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

8.7K views15:38

Python | Machine Learning | Coding | R

Surprise 2
https://t.me/+MhmkscCzIYQ2MmM8

Data Science Books

Admin and ads: @hussein_sheikho

6.7K views16:16

Python | Machine Learning | Coding | R

🖥 Generate API docs under a minute in Django

https://t.me/CodeProgrammer

6.7K views16:17

Python | Machine Learning | Coding | R

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👁Savant: Supercharged Computer Vision and Video Analytics Framework on DeepStream

git clone https://github.com/insight-platform/Savant.git

cd Savant/samples/peoplenet_detector

git lfs pull

▪Github: https://github.com/insight-platform/Savant

https://t.me/CodeProgrammer

6.9K viewsedited 12:51

Python | Machine Learning | Coding | R

🖥 Convert PDF to docx using Python

▪Github: https://github.com/dothinking/pdf2docx

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6.7K viewsedited 12:28

Python | Machine Learning | Coding | R

🖥 Roadmap of free courses for learning Python and Machine learning.

▪Data Science
▪ AI/ML
▪ Web Dev

1. Start with this
https://kaggle.com/learn/python

2. Take any one of these

❯ https://openclassrooms.com/courses/6900856-learn-programming-with-python

❯ https://scaler.com/topics/course/python-for-beginners/

❯ https://simplilearn.com/learn-python-basics-free-course-skillup

3. Then take this
https://netacad.com/courses/programming/pcap-programming-essentials-python

4. Attempt for this certification
https://freecodecamp.org/learn/scientific-computing-with-python/

5. Take it to next level

❯ Data Scrapping, NumPy, Pandas
https://scaler.com/topics/course/python-for-data-science/

❯ Data Analysis
https://openclassrooms.com/courses/2304731-learn-python-basics-for-data-analysis

❯ Data Visualization
https://kaggle.com/learn/data-visualization

❯ Django
https://openclassrooms.com/courses/6967196-create-a-web-application-with-django

❯ Machine Learning
http://developers.google.com/machine-learning/crash-course

❯ Deep Learning (TensorFlow)
http://kaggle.com/learn/intro-to-deep-learning

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12.3K viewsedited 14:10

Python | Machine Learning | Coding | R

IMG_20190924_203115.jpg

367.3 KB

RoadMap: Text Mining (NLP Background)

https://t.me/CodeProgrammer

IMG_20190924_203119.jpg

370 KB

RoadMap: NLP Based On Deep Learning

https://t.me/CodeProgrammer

IMG_20190924_203112.jpg

209.1 KB

RoadMap: Porbability and Statistics

https://t.me/CodeProgrammer

IMG_20190924_203106.jpg

239.2 KB

RoadMap: Machine Learning

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ML_cheatsheets.pdf

6.5 MB

Machine Learning cheatsheet (very important)

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8.5K viewsedited 14:34

Python | Machine Learning | Coding | R

✋ Hand gesture recognition

Full Source Code 👇👇👇👇

6.2K viewsedited 10:15

Python | Machine Learning | Coding | R

✋ Hand gesture recognition Full Source Code 👇👇👇👇

✋ Hand gesture recognition

import cv2
import mediapipe as mp

# Initialize MediaPipe Hands module
mp_hands = mp.solutions.hands
hands = mp_hands.Hands()

# Initialize MediaPipe Drawing module for drawing landmarks
mp_drawing = mp.solutions.drawing_utils

# Open a video capture object (0 for the default camera)
cap = cv2.VideoCapture(0)

while cap.isOpened():
    ret, frame = cap.read()
    
    if not ret:
        continue
    
    # Convert the frame to RGB format
    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    
    # Process the frame to detect hands
    results = hands.process(frame_rgb)
    
    # Check if hands are detected
    if results.multi_hand_landmarks:
        for hand_landmarks in results.multi_hand_landmarks:
            # Draw landmarks on the frame
            mp_drawing.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS)
    
    # Display the frame with hand landmarks
    cv2.imshow('Hand Recognition', frame)
    
    # Exit when 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the video capture object and close the OpenCV windows
cap.release()
cv2.destroyAllWindows()

https://t.me/CodeProgrammer

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7.3K viewsedited 10:16

Python | Machine Learning | Coding | R

📈 Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide

The process of building a stock price prediction model using Python.

1. Import required modules

2. Obtaining historical data on stock prices

3. Selection of features.

4. Definition of features and target variable

5. Preparing data for training

6. Separation of data into training and test sets

7. Building and training the model

8. Making forecasts

9. Trading Strategy Testing

https://t.me/CodeProgrammer

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6.7K viewsedited 16:03

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