Projects
- Titanic Survival Exploration
- Predicting Boston Housing Prices
- Finding Donors for CharityML
- Creating Customer Segments
- Train a Smartcab to Drive
- Deep Learning Image Classification
- Capstone Project - DQN
See links for individual project requirements.
Description: In this optional project, you will create decision functions that attempt to predict survival outcomes from the 1912 Titanic disaster based on each passenger’s features, such as sex and age. You will start with a simple algorithm and increase its complexity until you are able to accurately predict the outcomes for at least 80% of the passengers in the provided data. This project will introduce you to some of the concepts of machine learning as you start the Nanodegree program.
Description: The Boston housing market is highly competitive, and you want to be the best real estate agent in the area. To compete with your peers, you decide to leverage a few basic machine learning concepts to assist you and a client with finding the best selling price for their home. Luckily, you’ve come across the Boston Housing dataset which contains aggregated data on various features for houses in Greater Boston communities, including the median value of homes for each of those areas. Your task is to build an optimal model based on a statistical analysis with the tools available. This model will then used to estimate the best selling price for your client’s home.
Description: CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning. After nearly 32,000 letters sent to people in the community, CharityML determined that every donation they received came from someone that was making more than $50,000 annually. To expand their potential donor base, CharityML has decided to send letters to residents of California, but to only those most likely to donate to the charity. With nearly 15 million working Californians, CharityML has brought you on board to help build an algorithm to best identify potential donors and reduce overhead cost of sending mail. Your goal will be evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent.
Description: A wholesale distributor recently tested a change to their delivery method for some customers, by moving from a morning delivery service five days a week to a cheaper evening delivery service three days a week.Initial testing did not discover any significant unsatisfactory results, so they implemented the cheaper option for all customers. Almost immediately, the distributor began getting complaints about the delivery service change and customers were canceling deliveries — losing the distributor more money than what was being saved. You’ve been hired by the wholesale distributor to find what types of customers they have to help them make better, more informed business decisions in the future. Your task is to use unsupervised learning techniques to see if any similarities exist between customers, and how to best segment customers into distinct categories.
Description: In the not-so-distant future, taxicab companies across the United States no longer employ human drivers to operate their fleet of vehicles. Instead, the taxicabs are operated by self-driving agents — known as smartcabs — to transport people from one location to another within the cities those companies operate. In major metropolitan areas, such as Chicago, New York City, and San Francisco, an increasing number of people have come to rely on smartcabs to get to where they need to go as safely and efficiently as possible. Although smartcabs have become the transport of choice, concerns have arose that a self-driving agent might not be as safe or efficient as human drivers, particularly when considering city traffic lights and other vehicles. To alleviate these concerns, your task as an employee for a national taxicab company is to use reinforcement learning techniques to construct a demonstration of a smartcab operating in real-time to prove that both safety and efficiency can be achieved.
Description: In this project, you will use what you've learning about deep neural networks and convolutional neural networks to create a model that can classify images with the CIFAR-10 dataset. You'll use TensorFlow to create a model capable of classifying an image into one of 10 different categories, including cats, dogs, birds, trucks, automobiles, and more.
Description: Train an agent to drive an under-powered car up a steep hill using a Deep Q network (DQN). This project uses the MountainCar-v0 environment from OpenAI-gym.