What if machines could learn?

This has been one of the most intriguing questions in science fiction and philosophy since the advent of machines. With modern technology, such questions are no longer bound to creative conjecture. Machine learning is all around us. From deciding which movie you might want to watch next on Netflix to predicting stock market trends, machine learning has a profound impact on how data is understood in the modern era.

This tutorial aims to give you an accessible introduction on how to use machine learning techniques for your projects and data sets. In just 20 minutes, you will learn how to use Python to apply different machine learning techniques — from decision trees to deep neural networks — to a sample data set. This is a practical, not a conceptual, introduction; to fully understand the capabilities of machine learning, I highly recommend that you seek out resources that explain the low-level implementations of these techniques.

Our sample data set: passengers of the RMS Titanic. We will use an open data set with data on the passengers aboard the infamous doomed sea voyage of 1912. By examining factors such as class, sex, and age, we will experiment with different machine learning algorithms and build a program that can predict whether a given passenger would have survived this disaster.