MACHINE LEARNING

Detailed explanation-1: -Explanation : A demographic dataset with statistics on different cities’ population, GDP per capita, economic growth is an example of “structured” data by opposition to image, audio or text datasets.

Detailed explanation-2: -Unsupervised or undirected data science uncovers hidden patterns in unlabeled data. In. The objective of this class of data science techniques, is to find patterns in data based on the relationship between data points themselves. An application can employ both supervised and unsupervised learners.

Detailed explanation-3: -Unsupervised learning: Unsupervised learning deals with the unlabeled data. No training data set is provided which means, no training will be given to the machine.

Detailed explanation-4: -What is unsupervised learning? Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention.

Detailed explanation-5: -The most commonly used unsupervised learning algorithms are: K-means clustering. Hierarchical clustering. Apriori algorithm.