MLDB ships with some demo and tutorial Notebooks.
The Benchmarking MLDB notebook shows how to run the The Absolute Minimal Machine Learning Benchmark with MLDB.
The Transfer Learning with Tensorflow demo demonstrates how to do transfer learning to leverage the power of a deep convolutional neural network without having to train one yourself. Most people do not train those types of networks from scratch because of the large data and computational power requirements. What is more common is to train the network on a large dataset (unrelated to our task) and then leverage the representation it learned.
This topic is broken down in two demos. First, the Image Processing with Convolutions demo explains convolutions and shows different ways to do them with MLDB. Second, the Real-Time Digits Recognizer demo goes though the machine learning concepts necessary to build the MLPaint plugin.
The Predicting Titanic Survival demo shows a classification workflow:
The Mapping Reddit and Visualizing StackOverflow Tags demos show how to use MLDB to visualize high-dimensional datasets:
The Recommending Movies demo shows how to use MLDB to do recommendation with the
svd.train procedure type.
The Exploring Favourite Recipes demo shows how to use MLDB to do clustering/topic extraction among recipes with the
kmeans.train procedure type.
The Enron Spam Filtering demo uses MLDB to show the perils of over-reliance on Area Under the Curve as a metric for evaluating classifiers.
The Mapping Election Press Releases demo uses MLDB to visualize the relationships between texts with Word2Vec using the
import.word2vec procedure type.
The Investigating the Panama Papers demo shows off MLDB's SQL engine by exploring the raw data from the Offshore Leaks Database.