Supervised vs Unsupervised Learning Explained

Machine learning plays a crucial role in various fields, including social media, healthcare, and finance, with supervised and unsupervised learning being two primary training approaches for models. Supervised learning involves training models using labeled data to predict outcomes or classify new data, relying on human oversight to label the data, making it resource-intensive. It is often applied in scenarios like spam detection, image classification, and predictive analytics for forecasting trends. On the other hand, unsupervised learning deals with unlabelled data to identify patterns and trends, often used for clustering data or understanding relationships between data points without human intervention. This approach is beneficial in exploratory data analysis, market segmentation, and anomaly detection. The Seldon Deploy platform supports both types of machine learning, highlighting the importance of understanding the differences between them to effectively deploy models based on organizational needs and available data.

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