AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

The reasoning behind selecting Numpy as the option that is not considered a machine learning technique stems from its primary function as a library focused on numerical computation rather than direct machine learning applications. Numpy is invaluable for performing array operations, linear algebra, and numerical computations efficiently, serving as a backbone for many other libraries.

In contrast, the other libraries mentioned play a more active role in machine learning tasks. Pandas is designed for data manipulation and analysis, making it crucial for data preprocessing in machine learning workflows. SciPy builds on Numpy and provides additional functionality for scientific and technical computing, which is often applicable in machine learning contexts as it includes optimization, integration, interpolation, and other techniques that can be used in modeling and analysis. Scikit-Learn, specifically, is a widely-used library for machine learning that provides tools for classification, regression, clustering, and various preprocessing functions directly tailored for machine learning tasks.

Thus, emphasizing Numpy's core focus on numerical data handling rather than specific machine learning functionalities explains why it is aptly identified as the library not categorized under machine learning techniques.