This month, we look at two very common supervised methods in the context of machine learning: linear support vector machines (SVMs) and k-nearest neighbors (kNNs).

Artificial intelligence (AI) and machine learning (ML) are transforming our world. When it comes to these concepts there are important differences between supervised and unsupervised learning.

Supervised machine learning is a branch of AI. This article covers the relevant concepts, importance in various fields, practical use in investing, and CAPTCHA applications.

In machine learning problems where supervised learning might be a good fit but there’s a lack of quality data available, semi-supervised learning offers a potential solution.

An AI machine learning method that trains a neural network by feeding it predefined sets of inputs. Sometimes used in the pre-training phase but mostly employed when the model is fine-tuned ...

In supervised learning, the most prevalent, the data is labeled to tell the machine exactly what patterns it should look for. Think of it as something like a sniffer dog that will hunt down ...

Use modern machine learning tools and python libraries. Explain how to deal with linearly-inseparable data. Compare logistic regression’s strengths and weaknesses. Explain what decision tree is & how ...

Like other machine learning methodologies, semi-supervised learning can face issues with data quality, incorrect predictions, or bias based on the labeled data provided.