01. Introduction to Unsupervised Learning

What is unsupervised machine learning?

• Unsupervised machine learning requires labeled data for training.

• Unsupervised machine learning is a method where algorithms learn from unlabeled data to find hidden patterns or intrinsic structures.

• It is a method that only focuses on supervised learning techniques.

• Unsupervised machine learning is used exclusively for classification tasks.

How does unsupervised learning differ from supervised learning?

• Supervised learning is faster than unsupervised learning.

• Unsupervised learning is a type of supervised learning.

• Unsupervised learning requires labeled data, while supervised learning uses unlabeled data.

• Unsupervised learning uses unlabeled data, while supervised learning uses labeled data.

What are some common algorithms used in unsupervised learning?

• Decision Trees

• K-means, Hierarchical Clustering, PCA, Apriori, Eclat

• Support Vector Machines

• Linear Regression

What is clustering in the context of unsupervised learning?

• Clustering involves labeling data points with predefined categories.

• Clustering is the process of grouping similar data points in unsupervised learning.

• Clustering is the process of sorting data points in ascending order.

• Clustering is a method for supervised learning.

Can you name a popular clustering algorithm?

• K-means

• Linear Regression

• Decision Tree

• Support Vector Machine

What is dimensionality reduction and why is it important?

• Dimensionality reduction is the process of reducing the number of features in a dataset, which is important for simplifying models, improving performance, and enhancing visualization.

• Dimensionality reduction is a method to increase model complexity.

• Dimensionality reduction is only useful for image processing tasks.

• Dimensionality reduction increases the number of features in a dataset.

What is the purpose of the k-means algorithm?

• To increase the dimensionality of the data.

• To sort data in ascending order.

• To find the average value of a dataset.

• To partition data into k distinct clusters based on similarity.

How does hierarchical clustering work?

• Hierarchical clustering sorts data points in a linear fashion.

• Hierarchical clustering randomly assigns data points to clusters.

• Hierarchical clustering only works with numerical data.

• Hierarchical clustering groups data points into a tree of clusters based on their similarity.

What is the role of the silhouette score in clustering?

• The silhouette score measures the number of clusters in a dataset.

• The silhouette score indicates the average distance between all points in a cluster.

• The silhouette score is used to determine the optimal number of features in a model.

• The silhouette score helps evaluate the quality of clustering by measuring how similar an object is to its own cluster versus other clusters.

What is anomaly detection in unsupervised learning?

• Anomaly detection is the process of labeling data points in a supervised manner.

• Anomaly detection is the process of identifying unusual patterns or outliers in data without prior labeling.

• Anomaly detection is used to enhance the accuracy of labeled datasets.

• Anomaly detection involves clustering similar data points together.