Machine Learning Basics

Jul 8, 2024

Machine Learning Basics

Introduction

  • Machine Learning (ML): Training machines to learn from past data and perform tasks faster than humans.
  • Key Components: Learning, understanding, and reasoning.

Example of Machine Learning

  • Paul's Song Preferences:
    • Factors: Tempo (x-axis), Intensity (y-axis)
    • Likes: Fast tempo and soaring intensity
    • Dislikes: Relaxed tempo and light intensity
  • Classification: Predicting song preference based on past data
    • Song A: Fast tempo, soaring intensity (Paul likes)
    • Song B: Medium tempo, medium intensity (Unclear preference)
    • Solution: Majority voting within a circle around the new point (k-nearest neighbors algorithm)

Key Machine Learning Concepts

  • Model Building: Learns from data, builds prediction models, more data leads to better accuracy.
  • Types of Learning:
    • Supervised Learning: Uses labeled data to train the model.
      • Example: Predicting currency based on coin weight (feature: weight, label: currency)
    • Unsupervised Learning: Uses unlabeled data to find patterns/clusters.
      • Example: Cricket player performance clustering (feature: runs and wickets, clusters: batsmen, bowlers)
    • Reinforcement Learning: Learning based on feedback (rewards/punishments)
      • Example: Correcting misidentified images through feedback.

Machine Learning Workflow

  • Input → Model → Output
    • Correct Output: Final result
    • Incorrect Output: Feedback loop for retraining

Quiz (Identify Learning Type)

  1. Facebook photo tagging -> Supervised Learning
  2. Netflix movie recommendations -> Supervised Learning
  3. Bank fraud detection -> Unsupervised Learning

Enablers of Modern Machine Learning

  • Abundant data: Generated from online activities and transactions
  • Improved memory handling and computational power of computers

Applications of Machine Learning

  • Healthcare: Predictive diagnostics for review
  • Social Media: Sentiment analysis
  • Finance: Fraud detection
  • E-commerce: Predicting customer churn
  • Taxi Services: Surge pricing models (e.g., Uber), predictive demand modeling

Conclusion

  • Machine Learning is integral in various fields and applications.
  • Encouragement to share examples from everyday life.

Keep watching this space for more interesting videos. Happy learning!