Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 135 discussion

Binary classification Supervised learning (Binary classification involves predicting one of two classes, and it requires labeled data for training.) Multi-class classification Supervised learning (Multi-class classification involves predicting one of multiple classes, and it also requires labeled data for training.) K-means clustering Unsupervised learning (K-means clustering is a technique used to group data into clusters without labeled data, making it an unsupervised learning method.) Dimensionality reduction Unsupervised learning (Dimensionality reduction techniques, such as PCA (Principal Component Analysis), are used to reduce the number of features in a dataset without labeled data, making it unsupervised.)

Supervised Learning: • Binary Classification: Requires labeled data (two classes) to train the model. • Multi-Class Classification: Requires labeled data (more than two classes) to train the model. Unsupervised Learning: • K-means Clustering: Does not require labeled data; it identifies natural groupings in the data. • Dimensionality Reduction: Typically unsupervised; it reduces the number of features based on the inherent structure of the data without using labels.

Binary classification - supervised learning Multi-class classification - supervised learning Both techniques involved training models with labeled data K-means clustering - unsupervised learning groups data based on similarity but not labels Dimensionality reduction - unsupervised learning aim to reduces number of features in dataset and does not need labels