Machine Learning Projects Summary

Jul 29, 2024

Machine Learning Projects Overview

In this presentation, we discussed machine learning projects categorized into three levels: beginner, intermediate, and advanced, using references from Ryan Reynolds' characters.

Importance of Projects

  • Projects are key to advancing skills in any development field, especially in machine learning, deep learning, and data science.
  • They allow for rapid skill progression from zero to expertise.
  • It is essential to showcase completed projects on platforms like GitHub to enhance career opportunities.

Beginner Level Projects (Green Lantern Style)

  1. Predicting Churn

    • Objective: Predict if a customer will stay (0) or leave (1) using tabular data.
    • Importance: Helps businesses retain customers, which is more cost-efficient than acquiring new ones.

  2. Forecasting Sales

    • Objective: Estimate future sales based on various features such as promotions and seasonal factors.
    • Output: A continuous value predicting sales amounts using regression models.

  3. Sentiment Analysis with Twitter API

    • Objective: Analyze tweets for sentiment (positive or negative).
    • Tools: Use NLTK library in Python to work with API data and process JSON responses.
    • Output: Predictions of sentiment on a scale from zero to one.

Intermediate Level Projects (Deadpool Style)

  1. Automatic Number Plate Detection

    • Uses computer vision to recognize license plates via object detection and optical character recognition (OCR).

  2. Text Generation Using Transformer Models

    • Leverages Hugging Face library to generate text summaries or creative content using advanced transformer models.

  3. Exercise Correction Using Key Point Detection

    • Utilizes MediaPipe for real-time tracking of exercises (e.g., bicep curls) to provide form corrections.

  4. Comment Toxicity Classification

    • Objective: Classify the toxicity of comments, similar to moderation tools used by platforms like Facebook.
    • Method: Tokenization and potential custom deep learning model development.

Advanced Level Projects (Dude Style)

  1. Image Super Resolution

    • Objective: Enhance low-resolution images to high-resolution using Generative Adversarial Networks (GANs).

  2. Building Game AI Using Reinforcement Learning

    • Teach an AI to play games (e.g., Flappy Bird) using reinforcement learning models.
    • Previous projects include training models to control spaceships and optimize shower temperatures.

  3. Neural Machine Translation

    • Build models to translate text between languages using sequential token generation.

  4. Action Recognition

    • Advanced object detection project focusing on identifying actions from sequences of frames (e.g., gesture recognition).

  5. Neural Style Transfer

    • Overlay artistic styles from famous paintings onto images using GANs for creative applications.

Conclusion

  • A wrap-up of the presented projects and their categorization from beginner to advanced levels.
  • Encouragement to explore these projects to build skills and enhance portfolios.