This repository contains Udacity's Machine Learning Engineering Nanodegree classroom projects.
This README is broken down into the following sections:
- Titanic Survival Exploration
- Boston Housing
- Customer Segments
- Digit Recognition
- Finding Donors
- Smartcab
- Dog Breed Recognition
- Capstone
Extra Projects
In this project, we created decision functions that attempt to predict survival outcomes from the 1912 Titanic disaster based on each passenger’s features, such as sex and age. We started with a simple algorithm and increase its complexity until we were able to accurately predict the outcomes for at least 80% of the passengers in the provided data.
The Boston housing market is highly competitive, and we want to be the best real estate agent in the area. To compete with our peers, we decided to leverage a few basic machine learning concepts to assist us and a client with finding the best selling price for their home. Luckily, we’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. Our task is to build an optimal model based on a statistical analysis with the tools available. Then, This model was used to estimate the best selling price for our clients' homes.
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.
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. Our task is to use unsupervised learning techniques to see if any similarities exist between customers, and how to best segment customers into distinct categories.
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 depend on smartcabs to get to where they need to go as safely and reliably as possible. Although smartcabs have become the transport of choice, concerns have arose that a self-driving agent might not be as safe or reliable as human drivers, particularly when considering city traffic lights and other vehicles. To alleviate these concerns, our 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 reliability can be achieved.
In this project, we learned how to build a pipeline to process real-world, user-supplied images. Given an image of a dog, our algorithm identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.
Think about a technical field or domain that you are passionate about, such as robotics, virtual reality, finance, natural language processing, or even artificial intelligence (the possibilities are endless!). Then, choose an existing problem within that domain that you are interested in which you could solve by applying machine learning algorithms and techniques. Be sure that you have collected all of the resources needed (such as data sets) to complete this project, and make the appropriate citations wherever necessary in your report. Below are a few suggested problem areas you could explore if you are unsure what your passion is: