---
title: "10 Machine Learning Terms Asked in Interviews"
description: "The machine learning projects comprise a series of steps. In this post, I have shared the Dataset terminology for your quick reference."
url: https://srinimf.com/2020/09/27/16-machine-learning-data-terminology/
date: 2020-09-27
modified: 2026-10-12
author: "Srini"
image: https://i0.wp.com/srinimf.com/wp-content/uploads/2020/09/pexels-photo-374820.jpeg?fit=1880%2C1253&ssl=1
categories: ["Machine Learning"]
tags: ["ML Model", "Terminology"]
type: post
lang: en
---

# 10 Machine Learning Terms Asked in Interviews

Here’re the ten simplified ***meanings [of Machine](https://srinimf.com/2016/06/26/3-types-of-commonly-used-algorithms-for-any-data-problem/) Learning Key Terms. ***Frankly, you need these terms memorized. Whether you work as a professional or search for a job, these terms matter. ML provides a framework for solving real-life problems and offers valuable insights.

Related[Must Read Jokes! On Software Delivery Date](https://srinimf.com/2018/11/23/software-comics-on-delivery-date-by-srinimf/)

## Machine learning key terms

| Word | Definition |
| --- | --- |
| 1. instance or example | A single object, observation, transaction, or record. |
| 2. target or label | The numerical or categorical attribute of interest. A model predicts this variable for each new instance. |
| 3. features | The input attributes that a model uses to predict the target. These attributes may be numerical or categorical. |
| 4. model | A mathematical object that describes the relationship between the features and the target. |
| 5. training data | The set of instances with a known target that developers use to fit an ML model. |
| 6. recall | Using a model to predict a target or label. |
| 7. supervised machine learning | Supervised machine learning uses examples with known output values to infer a function that relates input values to the output. |
| 8. unsupervised machine learning | Unsupervised machine learning analyzes unlabeled examples to find hidden structure instead of relying on labeled examples. |
| 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 the system makes a prediction and updates the model for each new example. |

*Machine Learning Terminology on Data*

Related[Here’s Best Example of ML to Know Person’s Marital Status](https://srinimf.com/2019/12/28/ml-model-to-predict-a-person-is-married-or-not/)

**Also read: Go**ogle Cloud Certification

## Supervised Vs. Unsupervised

| Instances (Examples) | M/F (Attribute-1) | Salary (Attribute-2) | Target (Label) |
| --- | --- | --- | --- |
| instance-1 | M | 10,000 | Eligible for Bonus |
| Instance-2 | F | 20,000 | Not Eligible for Bonus |
| | | | |

*Sample Data*

Here, the predictable value (Eligibility) has a predefined value. If you know the Target upfront, you use supervised learning. In unsupervised learning, you do not know the target; instead, you use the data to discover hidden patterns and make predictions.

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