Supervised and unsupervised learning in AI

Let's learn the two basic approaches of data science, the supervised learning and the unsupervised learning.

What is supervised learning?

Supervised learning is a machine learning approach that uses labeled datasets. These datasets are designed to predict the outcomes accurately. Using labeled inputs and outputs, the model can measure its accuracy and learn over time.

Supervised learning can be separated into two types of problems.

classification and Regression

Classification problems uses an algorithm to accurately assign test data to specific categories such as separating apples from oranges.

Regression is another type of supervised learning method that uses an algorithm to understand the relationship between dependent and independent variables. Regression models are helpful in predicting numerical values based on different data points.

what is unsupervised learning?

Unsupervised learning uses machine learning algorithms to analyze and cluster unlabeled datasets.

These algorithms discover hidden patterns in data.

Unsupervised learning models are used for three main tasks: clustering , associating and dimensionality reduction.

Clustering:

Clustering is a data mining technique for grouping unlabeled data based on their similarities or differences.

Association:

Association uses different rules to find relationships between variables in a given dataset

Dimensionality reduction:

It's a learning technique used when the number of features in a given dataset is too high. It reduces the number of data inputs to a manageable size while also preserving the data