Machine Learning Models

In general, the term “model” refers to a simplified representation of something real. For example, when we use a simulation program to simulate an electrical circuit, the resulting representation is considered as a model for the physical hardware circuit. Using this model, we can understand the circuit, analyze it, and also make predictions.

So, what exactly do machine learning models represent?

Machine learning models represent patterns and relationships within specific datasets. These models enable machines to understand the data and make predictions for new, unseen data.

Machine Learning Process:

1) Algorithm Selection: We start by choosing an underlying algorithm.

2) Data Feeding: Next, we feed the machine with data.

3) Model Building: The machine employs the selected algorithm to understand the data and construct a model that represents it.

4) Continuous Refinement: As we provide more data, the machine continually adjusts this model. i.e., it refines its understanding of the data.

5) Prediction: the machine uses this model to make predictions for new, unseen data

The more data the machine learns from, the better its ability to predict accurate results. This process allows the model to represent real-world data effectively, enhancing the reliability of its predictions.