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  1. Machine learning models are algorithms designed to identify patterns or make predictions from data. These models can be broadly categorized into supervised, unsupervised, semi-supervised, and reinforcement learning models, each suited for specific tasks and data types.

    Supervised Learning Models

    Supervised learning uses labeled data to train models. It is divided into two main types:

    • Classification: Predicts discrete categories, such as spam detection or image classification. Common algorithms include Logistic Regression, Support Vector Machines (SVM), Decision Trees, and Random Forests.

    • Regression: Predicts continuous outcomes, such as house prices or stock trends. Popular algorithms include Linear Regression, Polynomial Regression, and Support Vector Regression (SVR).

    Unsupervised Learning Models

    Unsupervised learning works with unlabeled data to uncover hidden patterns:

    • Clustering: Groups similar data points, such as customer segmentation. Algorithms include K-Means and DBSCAN.

    • Dimensionality Reduction: Reduces feature space for visualization or faster computation, using techniques like Principal Component Analysis (PCA).

    • Anomaly Detection: Identifies outliers in data, useful for fraud detection. Examples include Isolation Forest and Local Outlier Factor.

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  3. 8 Machine Learning Models Explained in 20 Minutes - DataCamp

    • Tree-based models are supervised machine learning algorithms that construct a tree-like structure to make predictions. They can be used for both classification and regression problems. In this section, we will explore two of the most commonly used tree-based machine learning models: decision trees and random forests.
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