Projects for Machine Learning Nanodegree at Udacity
Machine learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence.
This program teaches how to become a machine learning engineer, and apply predictive models to massive data sets in fields like finance, healthcare, education, and more.
In this project, I 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. I started with a simple algorithm and increased its complexity until I was able to accurately predict the outcomes for at least 80% of the passengers in the provided data. This project introduces me to some of the concepts of machine learning as I start the Nanodegree program.
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. The 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 the client’s home.
As education has grown to rely more and more on technology, more and more data is available for examination and prediction. Logs of student activities, grades, interactions with teachers and fellow students, and more are now available in real time. Educators are after new ways to predict success and failure early enough to stage effective interventions, as well as to identify the effectiveness of different interventions. Toward that end, our goal is to model the factors that predict how likely a student is to pass their high school final exam.
Most of the data one collects doesn’t necessarily fit into nice, labeled categories. Many times not only is data not labeled, but categories are unknown! In this project we will take unstructured data, and then attempt to understand the patterns and natural categories that the data fits into. First we will learn about methods that are useful for dealing with data without labels, then we will apply this to a dataset of your choice, learning what natural categories sit inside it.
A smartcab is a self-driving car from the not-so-distant future that ferries people from one arbitrary location to another. In this project, we will use reinforcement learning to train a smartcab how to drive.
In this capstone project, we will leverage what we’ve learned throughout the Nanodegree program to solve a problem of your choice by applying machine learning algorithms and techniques. We will first define the problem we want to solve and investigate potential solutions and performance metrics. Next, we will analyze the problem through visualizations and data exploration to have a better understanding of what algorithms and features are appropriate for solving it.
We will then implement our algorithms and metrics of choice, documenting the preprocessing, refinement, and postprocessing steps along the way. Afterwards, we will collect results about the performance of the models used, visualize significant quantities, and validate/justify these values. Finally, we will construct conclusions about our results, and discuss whether our implementation adequately solves the problem.