An example project demonstrating the use of machine learning to identify microbe taxonomies from their DNA sequences.

The original lightweight introduction to machine learning in Rubix ML using the famous Iris dataset and the K Nearest Neighbors classifier.

Use the famous CIFAR-10 dataset to train a multi-layer neural network to recognize images of cats, dogs, and other things.

An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.

Use the K Nearest Neighbors algorithm to predict the probability of a divorce with high accuracy.

Build a classifier to predict the outcome of Dota 2 games with the Naive Bayes algorithm and results from 102,944 sample games.

Handwritten digit recognizer using a feed-forward neural network and the MNIST dataset of 70,000 human-labeled handwritten digits.

Recognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.

Demonstrating unsupervised clustering using the K Means algorithm and synthetic color data.

An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website.

An example project that predicts risk of credit card default using a Logistic Regression classifier and a 30,000 sample dataset.