If you are a newbie to Machine Learning(ML), you may find some of the terminologies a bit overwhelming. In order to understand what these terms mean, you need to understand their high-level meaning.
| Field | Type |
|---|---|
| Model | A representation of what a Machine Learning system has learned from the training. |
| Training | The process of building a Machine Learning model. Training is comprised of various examples to help to build the model. |
| Examples | One row of a dataset helps in training a model. An example consists of the input data and a Label. |
| Feature | A feature is an input variable that helps with the Prediction. |
| Label | In Supervised Learning, Label is the possible result of an example. |
| Dataset | A dataset is a collection of examples. |
| Prediction | Prediction is the output of a model based on the input examples. |
| Classification Model | A model that helps differentiate between two or more discrete models (e.g., spam and non-spam emails). |
| Supervised Learning | Supervised machine learning is about Training a model using the input data and respective Label. |
| Image Classification | A process that classifies objects and patterns in an image. |
| Unsupervised Learning | Unsupervised machine learning is about Training a model to find patterns in an unlabeled dataset. |
| Clustering | In Unsupervised machine learning, related examples are grouped together in a process called Clustering. |
| Regression Model | While a classification model outputs discrete values, a regression model outputs continuous values (e.g., values between 0 to 1). |
You can read more about these and other Machine Learning terminologies here.