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Linear Regression is a foundational algorithm for machine learning and statistical modeling. Traditionally, Linear Regression is the very first algorithm you'd learn when getting started with predictive modeling.

Types of Machine Learning (ML) Systems: 1. Based on Learning Style: a. Supervised Learning Learns from labeled data. Goal: Predict output for new inputs. Common tasks: Classification, Regression. Examples: Linear Regression, Decision Trees, SVM. b. Unsupervised Learning Learns from unlabeled data. Goal: Discover hidden patterns. Common tasks: Clustering, Dimensionality Reduction. Examples: K-Means, PCA, DBSCAN. c. Semi-Supervised Learning Uses a small amount of labeled data large unlabeled data.

Types of Machine Learning (ML) Systems: 1. Based on Learning Style: a.

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Curve fitting is a fundamental concept in data analysis and machine learning, where the objective is to identify a mathematical function that best represents or approximates a set of observed data points. The chosen function can take many forms - linear, polynomial, exponential, or even nonlinear models like those used in neural networks, depending on the complexity of the underlying relationship. The core challenge lies in reducing the gap between the actual data points and the predicted curve.

Curve fitting is a fundamental concept in data analysis and machine learning, where the

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