Understanding Linear Regression and Various Mathematical Functions in Machine Learning

2026-07-13 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Novice, short

Summary

This article explains Linear Regression, a fundamental supervised machine learning algorithm used for predicting continuous numerical values by fitting a straight line to data, represented by y = mx + c. It highlights applications such as house price prediction and sales forecasting. Beyond Linear Regression, the text details several essential mathematical functions in machine learning, including linear, polynomial f(x) = ax² + bx + c, exponential f(x) = aeᵇˣ, and logarithmic f(x) = log(x) functions for various data relationships and transformations. It also covers the Sigmoid function σ(x) = 1 / (1 + e⁻ˣ) for binary classification, ReLU ReLU(x) = max(0, x) as a deep learning activation, and the Mean Squared Error (MSE) (1/n) Σ (Actual − Predicted)² as a common cost function. Mathematics forms the core foundation for developing and optimizing ML models.

Key takeaway

For AI students or data scientists building predictive models, understanding the mathematical foundations is crucial. You should master Linear Regression's y = mx + c formula and its applications, alongside functions like polynomial for non-linear data, Sigmoid for classification, and ReLU for deep learning. This knowledge enables you to select appropriate algorithms and optimize model performance effectively.

Key insights

Mathematics underpins machine learning, enabling algorithms to analyze data and make predictions.

Principles

• Linear Regression predicts continuous values.
• Diverse functions handle varied data relationships.
• Cost functions quantify model prediction error.

Method

Linear Regression minimizes prediction error by fitting a straight line y = mx + c to data, using a cost function like MSE (1/n) Σ (Actual − Predicted)² to evaluate performance.

In practice

• Use Sigmoid for binary classification.
• Apply ReLU for neural network activation.
• Logarithmic functions scale features.

Topics

• Linear Regression
• Machine Learning Algorithms
• Mathematical Functions
• Predictive Modeling
• Cost Functions
• Deep Learning Activation

Best for: AI Student, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.