Nanodegree Program Syllabus:
P0: Titanic Survival Exploration
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.
P1: Predicting Boston Housing Prices
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.
P2: Build a Student Intervention System
A local school district has a goal to reach a 95% graduation rate by the end of the decade by identifying students who need intervention before they drop out of school. As a software engineer contacted by the school district, your task is to model the factors that predict how likely a student is to pass their high school final exam, by constructing an intervention system that leverages supervised learning techniques. The board of supervisors has asked that you find the most effective model that uses the least amount of computation costs to save on the budget. You will need to analyze the dataset on students’ performance and develop a model that will predict the likelihood that a given student will pass, quantifying whether an intervention is necessary.
P3: Creating Customer Segments
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.
P4: Train a Smartcab to Drive
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.
P5: Capstone Project
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. Finally, you will construct conclusions about your results, and discuss whether your implementation adequately solves the problem.
In this project, you will update your resume according to the conventions that recruiters expect and get tips on how to best represent yourself to pass the “6 second screen”. You will also make sure that your resume is appropriately targeted for the job you’re applying for. We recommend all students update their resumes to show off their newly acquired skills regardless of whether you are looking for a new job soon.
Technical Interview Practice
For this project, you will be given five technical interviewing questions on a variety of topics discussed in the technical interviewing course. You should write up a clean and efficient answer in Python, as well as a text explanation of the efficiency of your code and your design choices. A qualified reviewer will look over your answer and give you feedback on anything that might be awesome or lacking-is your solution the most efficient one possible? Are you doing a good job of explaining your thoughts? Is your code elegant and easy to read?
Why Take This Nanodegree Program?
This program will equip you with key skills that will prepare you to fill roles with companies seeking machine learning experts (or to introduce machine learning techniques to their organizations). Machine learning is literally everywhere, and is often at work even when we do not realize it. Google Translate, Siri, and Facebook News Feeds are just a few popular examples of machine learning’s omnipresence. The ability to develop machines and systems that automatically improve, puts machine learning at the absolute forefront of virtually any field that relies on data.