suresh Difference between Supervised and Unsupervised Machine Learning
Supervised and unsupervised machine learning are two main types of machine learning techniques. The key difference between the two lies in the availability of labeled data during the training process.
Supervised Machine Learning:
In supervised machine learning, the model is trained on a labeled dataset, where each data point is assigned a specific target or output. The goal of supervised learning is to predict the correct output when given new input data. Common examples of supervised machine learning algorithms include Linear Regression, Logistic Regression, Support Vector Machines, and Decision Trees.
Unsupervised Machine Learning:
On the other hand, unsupervised machine learning involves training the model on an unlabeled dataset. In this case, the algorithm learns patterns and relationships in the data without explicit guidance. The goal of unsupervised learning is to uncover hidden structures or insights within the data. Examples of unsupervised machine learning algorithms include K-means Clustering, Hierarchical Clustering, and Principal Component Analysis (PCA).
In summary, supervised machine learning requires labeled data for training, while unsupervised machine learning works with unlabeled data to discover patterns and relationships. Both approaches have their own applications and advantages based on the nature of the dataset and the desired outcomes.
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