How Machines Learn? - tech9tel/ai GitHub Wiki

🤖 How Machines Learn

Machines learn by finding patterns in data and then using those patterns to make predictions or decisions.

There are different ways machines learn, and each way helps the machine improve its ability to perform a task over time.

🧠 Key Concepts in Machine Learning

1. Training Data

   • Machines learn from data, which includes examples and outcomes. This data helps them understand patterns or relationships.
   • Example: In an email filter, data might include emails labeled as "spam" or "not spam."

2. Algorithms

   • Algorithms are like instructions or methods that machines use to learn from data.
   • The algorithm tries to find patterns that can help solve the task.
   • Example: A decision tree is an algorithm that helps make decisions based on different conditions.

3. Models

   • A model is the result of training the machine. It’s the machine’s understanding of the patterns in the data.
   • Example: After training, a model can predict if an email is spam or not based on past examples.

4. Learning Process

   • Training: The machine is trained on the data. It looks at the input (like email content) and learns how to predict or classify it.
   • Testing: After training, the model is tested with new data to see how well it learned.
   • Improvement: Based on performance, the model can be tweaked or retrained with new data for better results.

🌱 In Summary

Machines learn by studying examples, applying algorithms, and improving over time. As they process more data, their models become better at making predictions or solving problems.