Here are some projects that I developed. I Hope they can give you insights in some form! Click on a project to go to its repository.

Deep learning

Machine learning

Train a Smartcab to Drive (Reinforcement Learning)

Reinforcement learning techniques were applied for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time. A Q-learning algorithm was implemented.

Creating Customer Segments (Unsupervised Learning)

A dataset containing data on various customers from a wholesale distributor in Lisbon, Portugal, was explored using unsupervised learning techniques. The project included the of algorithms such as principal component analysis and gaussian mixture models, alongside different visualization techniques.

Student Intervention System (Supervised Learning)

A model was built using supervised learning in order to identify students who might need early intervention before they fail to graduate. Initial data contained around 30 variables. Three models were selected and evaluated: support vector machines, decision trees and AdaBoosting (with decision trees).

Boston Housing (Supervised Learning Fundamental Concepts)

This project used data data collected from homes in suburbs of Boston, Massachusetts, from the UCI Machine Learning Repository. Fundamental machine learning concepts such as overfitting and model complexity were analyzed using a decision tree model.

Titanic Survival Exploration (Basic Data Exploration)

This project consisted of a basic exploration of data from the 1912 Titanic disaster. Decision functions were created based on each passenger’s features, such as sex and age. The technique applied in the project corresponds to a very simple and manual implementation of the decision tree model.