For a final year university project, a social media platform was developed enabling users to form communities, start discussions, and comment on them. Tested on CRUD and software engineering principles.

A custom back-end learning project involved creating a straightforward real-time messaging app. Users can chat one-on-one or in group chats, send text messages and images, view active users, etc.

A SaaS platform that leverages AI to enable users to generate various media types and have conversations. Developing this project allowed me to explore Stripe, Clerk authentication, and unique AI APIs.

My first major project using Supabase was a basic music streaming site. Users can upload songs, search and listen to music, as well as like the songs they enjoy.

A note-taking app where users can sign up, log in, and reset passwords easily. It supports rich text formatting, image additions, and publishing notes publicly. Users alse organize notes into nested notebooks.

In my second year of university, my group and I started an open-source learning platform project, introducing me to full-stack development. This app helps students manage tasks, assignments, exams, and store notes and resources.

Comparing various classification algorithms to predict whether an adult earns more than $50,000 a year. Emphasis is on feature engineering, data preprocessing with One-Hot Encoding, and model optimization through hyperparameter tuning.

Comparing various regression algorithms to predict house prices in relation to the distance from the coast. Emphasis is on feature engineering, data preprocessing with One-Hot Encoding, and model optimization through hyperparameter tuning.

Practicing various algorithms and techniques. This includes supervised, unsupervised, and reinforcement learning algorithms, as well as feature engineering, data preprocessing, and hyperparameter tuning.

Practicing various Reinforcement Learning algorithms and techniques. This includes Q-Learning, Deep Q-Learning, and Asynchronous Advantage Actor-Critic (A3C) algorithms.

Implementing algorithms from scratch such as the Nearest Neighbours algorithm. Requires an understanding of the Mathematics behind the algorithms and the ability to implement them.

Be able to use and implement algorithms, with the Lasso and inductive conformal prediction algorithms as examples.

Be able to use and implement algorithms, with the SVM, neural networks, and cross-conformal prediction algorithms as examples.

Implemented various algorithms and techniques learnt during the course, such as Nearest Neighbours, Conformal Prediction, Regression algorithms, data preprocessing, kernels, Neural Networks, SVMs, etc.

Focusing on learning generative models, using third-party models and using advanced techniques. This includes techniques such as transfer learning, LLM Agents, and Generative Models.