Python Learning Resources

1. timer2: A Simplified Python Function Scheduler

timer2 is a Python module designed to streamline scheduling of functions at specific times or at regular intervals. It offers a user-friendly alternative to the built-in `threading.Timer` module.

Key Features:

Single-Threaded Efficiency: Unlike `threading.Timer`, timer2 operates within a single thread, ensuring efficient resource management. This makes it ideal for managing numerous scheduled tasks without introducing unnecessary overhead.

Focus on Lightweight Tasks: timer2 is best suited for scheduling non-resource intensive operations. For computationally expensive tasks, consider offloading them to a separate execution pool (threads, multiprocessing, or message queues) using `timer2.apply_after` as demonstrated in the provided example.

In essence, timer2 empowers you to efficiently schedule Python functions without the complexity of managing multiple threads, making it a valuable tool for streamlining task automation in your applications.

Source https://github.com/ask/timer2

Image to Sound Converter - Python Project

This Python project converts photos to auditory sounds. It makes use of both speech synthesis and optical character recognition (OCR) to do this. This is how it works.

1. Image Processing: The project most likely extracts text from the image using an OCR package pytesseract
2. Text to Speech: The extracted text is then converted into spoken audio using a speech synthesis library like gTTS.
3. Output: The final audio file can be played or saved for later use.

This description provides a concise overview of your project's functionality and setup instructions.

Source https://github.com/Kalebu/image-to-sound-python-.git

Tkinter-Based Classroom Management System

This is a Python application you developed to assist teachers and tutors in managing their classrooms. Here are some key features:

• User Interface: Built with Tkinter, providing a graphical interface for easy interaction.
• Student Management:
  • Create and manage student profiles.
  • Potentially store student data in a database (details not mentioned in the description).
• Performance Tracking:
  • Generate reports on student performance (details not mentioned in the description).
  • Visualize student and class performance using pie charts and bar graphs.
• Additional Features:
  • Percentage calculator (likely for calculating grades).
  • Option to create accounts within the software (for user management?).

Source https://github.com/andrew-geeks/tkinter-classroom-management-system.git

Skin Disease Detection using Convolutional Neural Networks (CNNs)

The purpose of this project is to use Convolutional Neural Networks (CNNs) to diagnose skin problems using image analysis.

Problem: Treatment for skin problems must begin as soon as possible with accuracy.

Solution: This research suggests employing CNNs, a form of deep learning algorithm, to analyze photos and detect skin problems.

Benefits of CNNs:

• High Accuracy:strong> Literature suggests CNNs achieve high accuracy in various tasks, including medical image analysis.
• Image Analysis: CNNs excel at extracting relevant information from photos, making them ideal for detecting skin diseases.

This project delves into the application of CNNs to enhance the precision of skin disease identification, perhaps resulting in prompt diagnosis and improved patient results.

Source https://github.com/snehitvaddi/Skin-Disease-Detection-through-Image-Analysis.git

Facial Recognition System with Tkinter GUI (Python & OpenCV)

This project uses Python and OpenCV to construct a facial recognition system with an intuitive Tkinter GUI. Below is a summary of its features:

Training Phase:

1. User Input: The train.py script prompts the user to enter an ID and name.
2. Image Capture: Clicking the "Take Images" button opens the camera and captures 60 image samples of the person.
3. Data Storage: Captured images are saved in the "TrainingImage" folder. User information (ID and name) is stored in a CSV file named "StudentDetails.csv" within the "StudentDetails" folder.
4. Model Training: Clicking the "Train Image" button trains the model using the captured images. The trained model is saved as "Trainner.yml" in the "TrainingImageLabel" folder.

Recognition Phase:

1. Face Detection: Clicking the "Track Image" button opens the camera again for real-time face detection.
2. Recognition: If a face is recognized, the system displays the person's ID and name on the image.
3. Attendance Recording: Upon program exit (pressing 'q' or 'Q'), a CSV file containing the recognized person's details (name, ID, date, and time) is saved in the "Attendance" folder. This information is also displayed in the GUI window.

Usage:

• Enter the base currency details (e.g., "USD").
• Provide the target currency details (e.g., "EUR").
• Enter the exchange rate and the amount of the base currency to convert.
• View the converted amount along with the target currency symbol.
• Press "ENTER" to exit the program.

Overall, this project offers a user-friendly facial recognition solution complete with training, face detection, attendance tracking, and data storage features.

Source https://github.com/Engineervinay/Face-Recognition-Based-Attendance-System.git

Missing Person Tracking System with Image Processing and Machine Learning

This Python project tackles the critical issue of missing persons, particularly children, in India. It uses a PostgreSQL database, machine learning, and image processing to help law enforcement with their search operations.

Problem: Although successful, traditional investigative techniques take a lot of time and might not be applicable to people who have migrated.

Timely response is impeded by the tedious chore of manually examining CCTV footage for incidents involving missing persons.

Solution:

1. Case Registration:
   • A user-friendly GUI application built with PyQt5 allows law enforcement to register missing person cases.
   • It captures relevant details and stores them securely in a PostgreSQL database.
2. Public Participation:
   • A separate mobile application empowers citizens to anonymously submit photos of people they suspect might be missing or found begging.
   • This anonymity protects users from potential local threats.
3. Matching Images::
   • The system utilizes the K-Nearest Neighbors (KNN) algorithm to compare registered missing person images with user-submitted photos.
   • Potential matches are flagged for further investigation by law enforcement.

This project offers a technological approach to expedite missing person searches and potentially reunite families.

Source https://github.com/gaganmanku96/Finding-missing-person-using-AI.git

Combating Email Spam with Machine Learning (College Project)

With the goal of providing college students with the resources they need for email security, this project addresses the issue of email spam, which is only becoming worse.

Problem:

• Increased internet usage causes an increase in spam emails, which are frequently used for illicit purposes including fraud and phishing.
• Spammers utilize cunning strategies to deceive naive users by masquerading as legitimate emails.

Solution - Machine Learning for Spam Detection:

• This project explores machine learning techniques to identify spam emails.
• Students will delve into various machine learning algorithms and apply them to email datasets.
• By evaluating performance metrics like precision and accuracy, the project will identify the most effective algorithm for spam detection.

Learnings and Benefits:

This project empowers students with valuable skills in machine learning and email security. The developed model can potentially be used to filter spam emails and enhance user safety. This project provides a valuable learning experience for college students, equipping them to combat the growing menace of email spam.

Source https://github.com/Vatshayan/Spam-Detection-Project.git

Machine Learning-Based Disease Prediction System (Python)

This study explores the potential applications of machine learning for disease prediction using user-reported symptoms.

Important Technologies:

Artificial Intelligence: In order to find trends in medical data, this initiative analyzes it using machine learning techniques.

Scikit-learn ,or Sklearn: This robust Python library has features for a number of machine learning uses, including classification (in this case, illness prognosis).

Data: A dataset including information on over 4,000 diseases and their accompanying symptoms is used by the system.

Functionality:

• Users can input their symptoms into the system.
• The machine learning model analyzes the provided symptoms against the vast disease dataset.
• The system predicts potential disease matches based on the learned patterns.

Source https://github.com/Vatshayan/Final-Year-Disease-Prediction-Project.git

3rdiSlideshow

3rdiSlideshow is an application designed to seamlessly stream all 3rdi Images to a video output, displayed in fullscreen mode and synchronized with the original images' timing.

• Install Ubuntu 22.04 for Desktop. Note: Creating the bootable USB stick on Windows is necessary for proper functioning.
• Enter the boot menu by pressing F7 or Delete, then select the USB for boot.
• Enable Remote Desktop in Settings.
• Disable the keyring by setting the password to empty. Reboot the system and ensure that screen sharing functions properly (test using the Microsoft Remote Desktop app).

Source https://github.com/kylemcdonald/3rdiSlideshow

Carbon Emission Prediction using Machine Learning

This study investigates the concerning rise of carbon emissions in developing countries,

Objective

• Analyze CO2 emission trends in a country.
• Identify key factors influencing CO2 emissions related to energy resources.
• Utilize machine learning algorithms to predict future emission levels.

Methodology:

Data Analysis: Employ descriptive methods to identify trends and relationships between CO2 emissions and related factors.

Machine Learning: Apply machine learning algorithms to develop predictive models for future CO2 emission levels.

Source https://github.com/Vatshayan/Co2-Emission-Prediction-Using-Machine-Learning.git

Coventry University Attendance System (CUAS) - Alternative Approach to Student Sign-In

Problem

Students forgetting their student cards can hinder their ability to sign in for lectures, creating administrative burdens and potentially inaccurate attendance records.

Proposed Solution: CUAS (Coventry University Attendance System)

CUAS offers a convenient and alternative method for student sign-in, addressing the issue of forgotten cards.

Student Sign-In via QR Codes:

• Students use their smartphones' cameras to scan QR codes displayed in classrooms.
• A dedicated mobile app (Tracker) facilitates the QR code scanning process.

Web Application for Lecturers:

• Lecturers can utilize a web application to monitor student attendance in real-time.
• This streamlines attendance tracking and reduces manual processes.

Overall, CUAS presents a practical and user-friendly solution for student sign-in, enhancing the attendance management process at Coventry University.

Source https://github.com/jmsv/cu-attendance-system.git

Birthday Paradox Simulation in Python

This project uses Python simulations to investigate the intriguing Birthday Paradox. The Birthday Paradox is the counterintuitive idea that there's a good chance that two or more persons in a group of remarkably small numbers will have the same birthday.

Project Components:
Theoretical Probability Calculation (birthday_paradox_simulation.py):

• This script calculates the theoretical probability of a birthday match based on mathematical formulas.
• It prompts the user for the number of people and outputs the calculated probability.

Birthday Paradox Simulation (birthday_paradox_simulation.py):

• This script simulates the Birthday Paradox by generating random birthdays for a specified number of people.
• It checks if there are any shared birthdays within the simulated group.
• The script runs multiple simulations and calculates the probability of a birthday match based on the simulation results.
• By comparing the theoretical and simulated probabilities, this project provides an engaging way to understand the Birthday Paradox.

Source https://github.com/jjggu97/Birthday-paradox-simulation.git

Sophia: Your Friendly Telegram Chatbot (Python Project)

Looking to streamline customer service for your business? Look no further than Sophia, your friendly Python-powered chatbot designed for Telegram!

What Sophia Does:

Answers Common Questions: Equipped with a library of predefined responses, Sophia can handle routine inquiries, freeing up your time for more complex issues.

Moderates Inappropriate Messages: Sophia helps maintain a positive chat environment by filtering out offensive language.

Engages in Conversations (Private & Groups): Capable of interacting in both individual and group chats, Sophia provides a convenient touchpoint for your customers.

Source https://github.com/tihcavalcante/Sophia_bot.git

Text-to-Speech with Voice Style Control (Python/PyTorch App)

This Python/PyTorch application empowers you to synthesize realistic human speech with customizable voice styles!

Key Features:

• Voice Model Training (Optional): Train your own voice model to achieve a specific voice style (details not mentioned).
• Pre-Trained Voice Models: Utilize pre-trained voice models for immediate speech synthesis.
• Vocoder Integration: Integrate a vocoder (like HiFiGAN) to convert the model's output into high-quality audio. You can import your own vocoder models from external sources
• Text Input Options:
  • Single Line: Synthesize a single sentence.
  • Multi Line: Synthesize multiple sentences, preserving the line breaks between them.
  • Paragraph: Automatically split a paragraph into sentences for natural-sounding speech synthesis.
• Synthesis Control: Select the desired voice model (checkpoint), language, and vocoder from the Settings menu.

Overall, this project offers an adaptable framework for producing speech that is similar to that of a human using custom voice styles. It serves both consumers who wish to use pre-trained models for rapid text-to-speech generation and developers who want to train custom voice models.

Source https://github.com/BenAAndrew/Voice-Cloning-App.gi