Here are top ten Machine Learning Terminology. Data is prime source for any ML project. And the Terminology people call is something different in the context of ML.
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Machine Learning Terms and Definitions
|1. instance or example||A single object, observation, transaction, or record.|
|2. target or label||The numerical or categorical (label) attribute of interest. This is the variable to be predicted for each new instance.|
|3. features||The input attributes that are used to predict the target. These also may be numerical or categorical.|
|4. model||A mathematical object describing the relationship between the features and the target.|
|5. training data||The set of instances with a known target to be used to fit an ML model.|
|6. recall||Using a model to predict a target or label.|
|7. supervised machine learning||Machine learning in which, given examples for which the output value is known, the training process infers a function that relates input values to the output.|
|8. unsupervised machine learning||Machine-learning techniques that don’t rely on labeled examples, but rather try to find hidden structure in unlabeled data.|
|9. ML workflow||The stages in the ML process: data preparation, model building, evaluation, optimization, and prediction.|
|10. online machine learning||A form of machine learning in which predictions are made, and the model is updated, for each new example.|
Machine Learning Sample Data
|instance-1||M||10,000||Eligible for Bonus|
|Instance-2||F||20,000||Not Eligible for Bonus|
Here, the predictable value (Eligibility) is pre-defined. So, if you know the Target upfront, it is called Supervised Learning. In the case of unsupervised learning, the target is unknown; It is something to find hidden predictions.