Machine Learning Overview

Machine learning is a subset of artificial intelligence (AI) that involves algorithms that improve their performance as they are exposed to more data over time without explicit programming.

Applications

• Recommendation Engines:
  • Tailored ads based on user clicking habits.

Types of Machine Learning

There are two primary approaches to machine learning training:

1. Supervised Learning

• Definition: Requires a data scientist to train the algorithm using labeled inputs paired with desired outputs.
• Example: A shape with three sides is labeled a triangle.
• Goal: To enable the algorithm to predict the correct label for new inputs independently.
• Ideal Use Cases:
  • Binary classification
  • Multi-class classification
  • Regression modeling
  • Ensembles
• Characteristics:
  • More common technique
  • Provides accurate results
  • Can be complex, time-consuming, and expensive to compute

2. Unsupervised Learning

• Definition: Does not require labeled data. The algorithm analyzes unlabeled input data to find patterns and group data.
• Example: Grouping shapes based on the number of sides.
• Characteristics:
  • No teachers or correct outputs
  • The algorithm analyzes the underlying structure of data
  • Ideal Use Cases:
    • Clustering
    • Anomaly detection
    • Association mining
    • Dimensionality reduction
  • Less complex and operates in real-time
  • Generally less accurate compared to supervised learning

Summary

• Supervised learning is more prevalent and accurate but comes with higher complexity and costs.
• Unsupervised learning is simpler and faster but may yield less precise results.