Whenever something ML or DL-related is mentioned, those abbreviations often go together with the word “model.” That’s because when something ML or DL-related works, it’s always a model’s merit.
In AI, a model is a program or an algorithm that searches through datasets for patterns. It is like a blueprint that the AI system uses to understand the relationships between different variables and how they interact. Once trained, the model can draw specific inferences and make predictions based on data it hasn’t seen before. For example, a model trained on examples of credit card transactions can recognize fraud patterns and in the future, use these findings to detect attempts for fraudulent actions.
The quality of the model depends on how well it has been trained and how well it generalizes to new data. In essence, the model is the "brain" of the AI system, and its accuracy and effectiveness will determine the quality of the system's output.
Machine Learning (ML)
Machine Learning refers to a group of computer algorithms that can learn from examples and improve themselves without being explicitly coded by a human.
Deep Learning (DL)
Deep learning is a subfield of AI that uses algorithms inspired by the structure and function of the brain, called neural networks, to process and analyze even bigger amounts of data.
Model training in AI is like teaching a robot to recognize patterns and make predictions, just like we teach a child to recognize shapes, colors, and objects.