Machine learning development models can be categorized into three main types: supervised learning, unsupervised learning, and reinforcement learning. Each type has unique methods and applications. Supervised Learning Models Supervised learning models are trained on labeled data, making them ideal for predictive tasks. Linear Regression is used to predict continuous outcomes based on input features, such as predicting house prices. Decision trees classify data by splitting it based on feature values, which is useful in tasks like customer segmentation. Support Vector Machines (SVM) find the optimal boundary between classes, excelling in tasks like image recognition. Unsupervised Learning Models Unsupervised learning models work with unlabeled data, identifying patterns and structures—K-means Clustering partitions data into distinct groups based on similarity, aiding in market segmentation and image compression. Principal Component Analysis (PCA) reduces data...
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