madalinabuzau / udacity-machine-learning-nanodegree

My project submissions for the Machine Learning Nanodegree from Udacity.

My projects for the Machine Learning Engineering nanodegree

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This repo aims to be a portfolio of my work during the Machine Learning nanodegree of Udacity. I am very glad that I have enrolled in this project as I got to explore the fundamentals in all areas of machine learning: supervised, unsupervised, reinforcement, and deep learning fundamentals.
The assignments really prepare you for the work environment in this field. They were challenging, but overall, they made me a better researcher. Here is a brief description of the projects that you can find in this repository:

Project 1: Predicting Boston Housing Prices

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For this project, I've built a machine learning algorithm to predict the best selling price of a house in Boston. The dataset for this project originates from the UCI Machine Learning Repository. The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts. The final implementation required to train a model using the decision tree algorithm. To ensure that I am using an optimal model, I've trained the model using the grid search technique to optimize the 'max_depth' parameter for the decision tree.

Project 2: Find Donors for CharityML

The objective of this project was to help CharityML, a fictitious charity organization, to build an algorithm to best identify potential donors and reduce overhead cost of sending mail. My task was to 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.

Project 3: Creating Customer Segments

In this project I have implemented unsupervised learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data.

Project 4: Smartcab

The goal of this project was to construct an optimized Q-Learning driving agent that can navigate a Smartcab through its environment towards a goal: drive passengers from one location to another. The driving agent was evaluated on two important metrics: safety and reliability.

Project 5: Multidigit recognition

For this project I have designed and implemented a deep learning model that learns to recognize sequences of digits, using the Street View House Numbers (SVHN) dataset.

Capstone project: Diabetic retinopathy detection

For my capstone project, I've decided to work on diabetic retinopathy detection using deep learning. This project was based on the data provided by a competition held on Kaggle. My final model used a convolutional neural network and data augmentation techniques in order to improve the precision of the model.