If you are an aspiring data scientist, you may have come across the terms artificial intelligence (AI), machine learning, deep learning and neural networks.

Specialization: Machine LearningInstructor: Geena Kim, Assistant Teaching ProfessorPrior knowledge needed: Calculus, Linear algebra, PythonLearning Outcomes Explain what unsupervised learning is, and ...

Set up a supervised learning project, then develop and train your first prediction function using gradient descent in Java.

When a quantum computer processes data, it must translate it into understandable quantum data. Algorithms that carry out this 'quantum compilation' typically optimize one target at a time. However ...

Rooted in mathematics, the novel machine learning algorithm is called CEBRA (pronounced zebra), and learns the hidden structure in the neural code. What information the CEBRA learns from the raw ...

Learn the concepts of data science and machine learning, their special relationship and a few practical examples.

Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests.

This repository contains code and documentation for a project that demonstrates the classification of the Iris dataset using the Decision Tree Classifier algorithm. The Decision Tree is a popular and ...

A data privacy expert explains how machine learning algorithms draw inferences and how that leads to privacy concerns.

Discover the power of machine learning to analyze and make predictions on huge data, improve decision-making and enhance automation processes.