"Nanodegree" is a registered trademark of Udacity. Udacity is not an accredited university and we don't confer traditional degrees. Udacity Nanodegree programs represent collaborations with our industry partners who help us develop our content and who hire many of our program graduates.

Some videos are available only by enrolling in a Coursera or EdX class. It is free to do so, but sometimes the classes are no longer in session so you have to wait a couple of months, so you have no access. I'm going to be adding more videos from public sources and replacing the online course videos over time. I like using university lectures.

Some videos are available only by enrolling in a Coursera or EdX class. It is free to do so, but sometimes the classes are no longer in session so you have to wait a couple of months, so you have no access. I'm going to be adding more videos from public sources and replacing the online course videos over time. I like using university lectures.

Detecting anomalies is critical in conducting surveillance, countering credit-card fraud, protecting against network hacking, combating insurance fraud, and many more applications in government, business and healthcare. Sometimes, the analyst has a set of known anomalies, and identifying similar anomalies in the future can be handled as a supervised learning task (a classification model). More often, though, little or no such "training" data are available. In such cases, the goal is to identify cases that are very different from the norm. Some techniques (clustering, nearest neighbors) may be familiar to you, others less so (e.g. based on information theory or spectral techniques).

In this capstone project, you will leverage what you've learned throughout the Nanodegree program to solve a problem of your choice by applying machine learning algorithms and techniques. You will first define the problem you want to solve and investigate potential solutions and performance metrics. Next, you will analyze the problem through visualizations and data exploration to have a better understanding of what algorithms and features are appropriate for solving it. You will then implement your algorithms and metrics of choice, documenting the preprocessing, refinement, and postprocessing steps along the way. Afterwards, you will collect results about the performance of the models used, visualize significant quantities, and validate/justify these values.