Key Principles
Last updated
Last updated
Machine learning relies on data. The more data we have, the better the model can learn. Data helps the model understand patterns and make predictions.
A model is the core of machine learning. It’s a mathematical representation that the system builds by learning from data. Once trained, the model uses this knowledge to predict outcomes or classify data.
During the training process, the model is exposed to a dataset (called the training set) where the input and the expected output are known. The model uses this data to adjust itself and learn how to make accurate predictions. The goal of training is to minimize errors when the model predicts the output.
After training, the model is tested with a different set of data (called the testing set) to see how well it performs on data it hasn’t seen before. This helps measure how accurate and reliable the model is.
Features are the input data used to make predictions. For example, if we are predicting house prices, the size of the house, number of rooms, and location would be considered features.
Labels are the output data we are trying to predict. In the house price example, the label would be the actual price of the house.
Collect Data: You gather a set of data related to the problem you want to solve.
Train the Model: The machine learning algorithm uses this data to learn patterns. During training, the model adjusts its internal parameters to improve its accuracy.
Test the Model: After training, you test the model on new data to see if it can make good predictions.
Make Predictions: Once trained and tested, the model can predict outcomes on new, unseen data.
Overfitting: This happens when a model learns the training data too well, capturing even noise or random fluctuations. As a result, it might not perform well on new data.
Underfitting: This occurs when a model is too simple and cannot capture the patterns in the data properly.
Accuracy: A measure of how often the model makes correct predictions.
By understanding these basic principles, you can begin exploring how machine learning can solve real-world problems by finding patterns and making predictions based on data.
