Machine Learning Articles

Bushra Anjum

Updated on July 14, 2025

Choosing the right tool depends on your data type, goal, data volume and need for transparency. Our expert offers a framework for this decision.

Mike Thomas

Updated on July 14, 2025

These companies are fine-tuning their marketing efforts with machine learning.

Max Reynolds

Updated on July 11, 2025

The expectation-maximization (EM) algorithm is used to estimate model parameters when some data is missing or hidden. It iteratively assigns probabilities to hidden variables and updates parameters to maximize the likelihood of the observed data.

Mór Kapronczay

Updated on July 10, 2025

Mean squared error (MSE) and mean squared logarithmic error (MSLE) are loss functions used to evaluate regression model prediction accuracy. MSE prioritizes minimizing large absolute errors, and MSLE reduces outlier impact through logarithmic scaling.

Sparsh Gupta

Updated on July 09, 2025

Logistic regression is a classification algorithm that applies a sigmoid function to a linear model, converting outputs into probabilities. These probabilities are then used to classify data into binary categories.

Jayanta Adhikary

Updated on July 08, 2025

Ollama is an open-source platform that allows users to run LLMs locally using a REST API. Here’s how to use it.

Sadrach Pierre

Updated on July 08, 2025

Outlier detection is a data science technique with applications across a variety of industries. Here’s how to find outliers in data by using Python, with examples to illustrate the principles.

Ellen Glover

Updated on June 30, 2025

Artificial intelligence often makes decisions we don’t understand.

Giorgos Myrianthous

Updated on June 24, 2025

Diffusion models are generative AI models based on principles drawn from physics that are useful for image generation. Our expert explains how they work.

Jye Sawtell-Rickson

Updated on June 24, 2025

Computer vision is a field of artificial intelligence that trains computers to see, interpret and understand the world around them through machine learning techniques. Here’s how it works, why it matters, how it’s used and some challenges to keep in mind.