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difference between supervised and unsupervised learning

In the growing world of machine learning, two fundamental approaches reign supreme: supervised learning and unsupervised learning. These methodologies form the backbone of numerous applications, powering everything from recommendation systems to image recognition algorithms. However, despite their use, many individuals find it challenging to find the difference between the two. In this blog, we will talk about supervised and unsupervised learning, exploring their characteristics, applications, and key differences.

Supervised learning

Supervised learning involves training a model on a labeled dataset, where each input data point is paired with a corresponding target or output. The primary objective is for the model to learn the mapping between input features and the desired output. Through iterative optimization algorithms such as gradient descent, the model adjusts its parameters to minimize the discrepancy between its predictions and the actual targets.

Example: An example of supervised learning is email spam detection. By training a model on a labeled dataset containing examples of spam and non-spam emails, the model can learn to classify incoming emails as either spam or not spam based on their features.

Unsupervised Learning

In contrast, unsupervised learning involves training a model on an unlabeled dataset, where the algorithm must identify patterns or structures within the data without explicit guidance. The objective is to uncover inherent relationships or groupings among the input features, often through techniques such as clustering or dimensionality reduction.

Example: An example of unsupervised learning is customer segmentation. By applying clustering algorithms to a dataset containing customer transaction history and demographics, businesses can group customers with similar purchasing behavior or characteristics together, enabling targeted marketing strategies.