Project-based Learning

At Udacity, we believe in learning by doing. So, after learning the theory behind different machine learning algorithms—through a series of videos, text, and exercises—you'll be tasked with applying your knowledge to projects.

• Projects are, typically, practical code exercises that allow you to demonstrate some machine learning and data analysis skills.
• They are designed with the help of industry partners, so we make sure that we are teaching you the most relevant skills for becoming a machine learning engineer!
• When you complete each project, you will submit it to one of our project reviewers, who will give you personalized feedback on your code and execution.

To get a glimpse of the three projects you will create as a part of this program, take a look at the three videos below. Completing the projects will not only help you build your skills with what is taught in the lessons, but also show you how those skills are used in practice and build out your technical portfolio.

Don't worry if you are not familiar with how you would even approach some of the items discussed below, you will be learning the needed skills in the lessons ahead!

Project 1: Find Donors for CharityML with Kaggle [Supervised Machine Learning]

In this project, you will apply supervised learning techniques on data collected for the US census to help CharityML (a fictitious charity organization) identify groups of people that are most likely to donate to their cause. Finding potential donors for a charity involves analyzing data about the US population and grouping that population by similar interests/traits that can help identify likely donors.

Project 2: Create an Image Classifier [Deep Learning]

Learn how to build and train a neural network using the deep learning framework, PyTorch. You'll define and train a neural network that learns to classify images; going from image data exploration to network training and evaluation.

Project 3: Creating Customer Segments with Arvato [Unsupervised Machine Learning]

In this project, you will study a real dataset of customers for a company, and you'll apply several unsupervised learning techniques in order to segment customers into similar groups and extract information that may be used for marketing or product improvement.