Data warehousing is not a common method for deploying machine learning models; instead, models are typically deployed via REST APIs, batch processing, or embedded systems.

Data versioning is not a monitoring method; it is a practice used to manage changes in datasets over time.

Feature selection is the process of selecting a subset of relevant features, while Min-Max Scaling, Standardization, and Log Transformation are techniques for feature scaling.

Gradient Descent is an optimization algorithm, not a feature selection technique.

Cross-validation is a model evaluation technique, not a feature engineering technique.

Gradient Descent is an optimization algorithm, not a feature selection technique.

Image classification is typically a supervised learning task, while clustering is used for market segmentation, anomaly detection, and social network analysis.

Data Augmentation is a technique used during training, not a deployment strategy.

Data augmentation is a technique used during model training to increase the diversity of the training dataset, not a deployment strategy.

Gradient Descent is an optimization algorithm, not a feature engineering technique.

The Logistic kernel is not commonly used in Support Vector Machines; the other three are standard kernel functions.

Data labeling is a part of the data preparation process, not a key component of MLOps.

Linear regression cannot model complex relationships, which is a limitation, but it can handle numerical data and is sensitive to outliers.

Linear Regression is a modeling technique, not a feature extraction method.

Principal component analysis (PCA) is a dimensionality reduction technique, not a feature selection method.

Random linkage is not a recognized method of linkage in hierarchical clustering; the common methods include single, complete, and average linkage.

Calculating the silhouette score is not a step in the K-means algorithm; it is an evaluation metric used after clustering.

K-Means Clustering is an unsupervised learning algorithm, while the others are supervised learning algorithms.

K-Nearest Neighbors is a classification algorithm, not a clustering algorithm.

K-means linkage is not a type of hierarchical clustering; it refers to the K-means algorithm itself.

Support Vector Machine is a different type of machine learning algorithm, not a neural network architecture.

Support Vector Machine (SVM) is a different type of machine learning algorithm, not a neural network.

Clustering is an unsupervised learning technique, while classification and regression are types of supervised learning.

K-Means SVM is not a recognized type of SVM; the other options are valid types of Support Vector Machines.

Phrase tokenization is not a standard type of tokenization; the common types are word, sentence, and character tokenization.

Time series forecasting is not a typical application of clustering; it is more related to supervised learning.

Basic arithmetic calculations are not a typical application of neural networks, which are better suited for complex pattern recognition tasks.

While SVM can be used for regression, it is not typically the first choice for stock price prediction.

Data warehouses are primarily used for data storage and analysis, not for deploying machine learning models.

Predicting stock prices is not a typical use case for clustering, as it is a supervised learning task.