In supervised learning, we provide the algorithm with labeled training data, which means that we have both the input features and the corresponding output (the target variable or label). The main goal of supervised learning is to learn a relationship between the input and output, so that it can make predictions on new, unseen data. Examples of supervised learning algorithms include linear regression, logistic regression, and support vector machines.
On the other hand, in unsupervised learning, we only have input features and no corresponding output labels. The main goal here is to discover hidden patterns or structures within the data. Unsupervised learning algorithms are often used for tasks such as clustering, dimensionality reduction, and anomaly detection. Some examples include K-means clustering, hierarchical clustering, and principal component analysis (PCA).
In my experience, choosing between supervised and unsupervised learning depends on the problem at hand and the type of data available. If you have labeled data and a clear target variable to predict, supervised learning is the way to go. However, if you're exploring a dataset with no predefined labels or trying to uncover hidden patterns, unsupervised learning is a better choice.