In supervised machine learning algorithms, we have to provide labelled data, for example, prediction of stock market prices, whereas in unsupervised we need not have labelled data, for example, classification of emails into spam and non-spam.

In machine learning, the distinction between supervised and unsupervised learning lies primarily in the presence or absence of labeled data and the goal of the learning process.

Supervised Learning:
- Definition: Supervised learning involves training a model on a labeled dataset, where each input data point is associated with a corresponding target label.
- Goal: The primary goal of supervised learning is to learn a mapping from inputs to outputs, given a dataset consisting of input-output pairs. The model aims to generalize from the training data to make predictions on unseen data accurately.
- Examples: Common examples of supervised learning tasks include classification (where the output is categorical) and regression (where the output is continuous).
- Training Process: In supervised learning, the model is trained using algorithms that minimize the discrepancy between the predicted output and the actual target label.
Unsupervised Learning:
- Definition: Unsupervised learning involves training a model on an unlabeled dataset, where the algorithm tries to learn the underlying structure or patterns in the data without explicit guidance.
- Goal: The primary goal of unsupervised learning is to discover hidden patterns, structures, or relationships within the data. Unlike supervised learning, there are no predefined output labels to guide the learning process.
- Examples: Clustering (grouping similar data points together), dimensionality reduction (reducing the number of features while preserving the essential information), and anomaly detection are common examples of unsupervised learning tasks.
- Training Process: Unsupervised learning algorithms explore the data to find intrinsic structures or patterns. These algorithms typically involve techniques such as clustering, association rule learning, and principal component analysis (PCA).

In summary, the key difference between supervised and unsupervised learning lies in the presence or absence of labeled data and the nature of the learning task. Supervised learning deals with labeled data and aims to learn the mapping between inputs and outputs, while unsupervised learning works with unlabeled data to discover hidden patterns or structures within the data.