Supervised and unsupervised machine learning are two different approaches to working with datasets, and they have distinct characteristics. Here are the main differences between supervised and unsupervised machine learning datasets:

The supervised and unsupervised machine learning are two different approaches of machine learning. In supervised machine learning, the machine has to be trained and tested for modeling by the human. The dependent and independent variables have to be defined by the human, meaning that the input and output variables have to be decided by the human.

However, in unsupervised machine learning, the machine finds the pattern in your dataset. For example, in the cluster analysis, K-mean cluster, the machine finds the pattern of the data and gives the result.

The main difference between supervised and unsupervised machine learning datasets lies in the presence or absence of labeled target outcomes.

Supervised Learning Dataset: In a supervised learning dataset, each instance or example in the dataset comes with one or more labels or target values. The goal of supervised learning is to train a model that can predict these labels based on the input features.

For example, in a dataset for a binary classification problem, each instance might include a set of features (like a person's age, gender, and blood pressure) along with a binary label indicating whether or not the person has a particular disease. The goal would be to train a model that can predict the disease status based on the person's age, gender, and blood pressure.

In the case of regression, the labels might be continuous values rather than categories. For instance, a supervised learning dataset for predicting house prices might include features like the size of the house, the number of rooms, and the neighborhood, with the target label being the price of the house.

Unsupervised Learning Dataset: On the other hand, an unsupervised learning dataset does not include any target labels. The goal of unsupervised learning is not to predict a specific outcome, but to discover some underlying structure in the data.

For example, an unsupervised learning algorithm might be used to cluster customers into different segments based on their purchasing behavior, without any prior knowledge of what those segments might be. Or, it could be used to reduce the dimensionality of the data by finding a lower-dimensional representation that still captures the most important features of the data.

In summary, the key difference between a supervised and an unsupervised machine learning dataset is that the former includes target labels for each instance, while the latter does not.

I think the right terms are unsupervised and supervised technics or models. Unsupervised techniques mean that the algorithm will find patterns in your data (e.g. clustering and PCA) without indication for example you have pictures of apples, strawberries, or bananas the algorithm will categorise these pictures according to their similitude maybe the shape, colours but you give not information. In supervised techniques (regression, logistics, decision tree, random forest), the algorithm has a learning phase (the training) this time in addition to these pictures you label your data this is a strawberry, this is an apple. For the supervised method, your dataset will be split into training, validating, and testing sections. The training is to train your model for the learning phase, the validating to adjust specific parameters and monitoring the learning phase, and the testing to evaluate the model with metrics like accuracy.

This is a broad question, but the simple answer to it is that in supervised learning, the computer is given both input data and corresponding correct answers to learn from. On the other hand, in unsupervised learning, the computer is given only the input data without specific answers. It has to figure out patterns and relationships on its own.