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Machine Learning Algorithms: Key Models and Techniques
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This document covers the most widely used machine learning algorithms, including regression, classification, clustering, and neural networks. It explores supervised and unsupervised learning models, detailing how algorithms like decision trees, K-nearest neighbors, support vector machines, and rand...

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  • January 31, 2025
  • 5
  • 2024/2025
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Machine Learning Algorithms
Machine learning algorithms are the core techniques that allow computers to
identify patterns, make decisions, and generate predictions from data. Each
algorithm is designed to solve a specific type of problem, ranging from predicting
numerical values to identifying clusters or recognizing patterns in data.



Categories of Machine Learning Algorithms
Machine learning algorithms are broadly classified into three categories:

1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning



1. Supervised Learning Algorithms
Supervised learning involves training a model on labeled data, where the input
and corresponding output are provided. The model learns to map inputs to the
correct outputs.

Common Algorithms:

 Linear Regression: Predicts continuous values (e.g., house prices).
 Logistic Regression: Used for binary classification problems (e.g., spam
detection).
 Decision Trees: Splits data into branches to make decisions.
 Random Forests: Combines multiple decision trees to improve accuracy.
 Support Vector Machines (SVMs): Finds the optimal boundary to classify
data.
 Neural Networks: Mimics the human brain to solve complex problems.

, Example:
Predicting the likelihood of a customer buying a product based on their purchase
history.



2. Unsupervised Learning Algorithms
Unsupervised learning works with unlabeled data. The algorithm tries to find
hidden structures or patterns in the data without predefined outputs.

Common Algorithms:

 K-Means Clustering: Groups data points into clusters based on similarity.
 Hierarchical Clustering: Creates a hierarchy of clusters.
 Principal Component Analysis (PCA): Reduces dimensionality while
preserving variance.
 Anomaly Detection: Identifies unusual patterns or outliers.

Example:
Segmenting customers into groups based on purchasing behavior.

Fun Fact:
Netflix uses unsupervised learning to group similar movies and shows, making
recommendations based on your viewing history.



3. Reinforcement Learning Algorithms
Reinforcement learning is inspired by behavioral psychology. It trains agents to
make decisions by rewarding desirable actions and penalizing undesirable ones.

Key Components:

 Agent: The learner or decision-maker.
 Environment: Where the agent interacts.
 Actions: Choices available to the agent.
 Rewards: Feedback for each action.

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