Machine Learning (ML) helps computers learn from examples rather than following fixed instructions. It’s widely used in areas like healthcare, finance, transport, and cybersecurity, but how does it actually work? Here’s a simple step-by-step look:
1. Input Data
Every ML project begins with collecting data. This might include anything from photos and text to numbers in spreadsheets. The quality of this data really matters, so it’s important to tidy it up, fix errors, remove duplicates, and check it’s ready to use.
2. Look Into the Data
Before any model is trained, we need to understand the data we’ve gathered. This could mean spotting patterns, identifying missing pieces, or just seeing how the data behaves. Visual tools like charts and graphs help make sense of it all.
3. Learning Patterns
Next, we choose a method (called an algorithm) that will learn from the data. This could be a decision tree, a simple linear model, or something more complex like a neural network. The model picks up on trends and rules hidden in the data.
4. Making Predictions
Once training is done, we ask the model to make predictions. That might mean guessing tomorrow’s sales, recognising spoken words, or flagging suspicious activity. We test its results to see how close it gets to the truth.
5. Using the Results
Finally, the predictions are put to use. Whether it’s improving a service, making a faster decision, or simply sorting information, the goal is to support better outcomes with the help of data.
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