5 o’clock | Machine Learning Basics
Machine learning is broadly categorized into supervised and unsupervised learning based on how the model learns from data:
Supervised Learning: The model is trained using labeled data, meaning each input comes with a corresponding correct output. The algorithm learns by mapping inputs to outputs and is then tested on new data. Examples include:
- Linear Regression (predicting continuous values like house prices)
- Decision Trees (used for classification and regression tasks)
Unsupervised Learning: The model is given unlabeled data and must find patterns or relationships on its own. Instead of predicting specific outputs, it groups similar data points together or detects underlying structures. Examples include:
- Clustering methods (such as k-means, which organizes data into meaningful clusters)
- Dimensionality Reduction (like Principal Component Analysis, used to simplify complex datasets)
Common Algorithms
- Linear Regression: This algorithm finds a relationship between input variables and a continuous output (e.g., predicting salaries based on years of experience). It fits a straight line to the data and minimizes the error.
- Decision Trees: These are used for classification (categorizing data) or regression (predicting continuous values). They work by splitting data into branches based on decision rules—kind of like a flowchart.
- Clustering Methods: These algorithms help in unsupervised learning by grouping similar data points together. Examples include: K-Means Clustering (dividing data into ‘k’ groups based on similarities). Hierarchical Clustering (creating a tree-like structure of clusters)
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