This is a custom backend for the first iteration of the discussion platform. This was created to learn how to create a custom backend using Python and Flask.

A simple Flask app to learn how to create a RESTful API. This was a foundational project to learn how to create a back-end using Flask. This was helpful when creating the back-end for the discussion platform.

A simple Flask app to learn how to use JWT for authentication. This serves as a foundation to using JWT in other projects using Flask.

A simple Django app to learn how to use Django with tokens for authentication. This serves as a foundation to using Django in other projects.

A project 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.

A project 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 Machine Learning algorithms and techniques. This includes supervised, unsupervised, and reinforcement learning algorithms, as well as feature engineering, data preprocessing, and hyperparameter tuning. Some additional content in the field of Deep Learning and Neural Networks are also covered.

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

Be able to implement machine-learning algorithms, using the Nearest Neighbours algorithm as an example. Have an understanding of ways to apply the ideas and algorithms of machine learning in science and technology.

Be able to use and implement machine-learning algorithms, with the Lasso and inductive conformal prediction algorithms as examples. Have an understanding of ways to apply the ideas and algorithms of machine learning in industry and medicine.

Be able to use and implement machine-learning algorithms, with the SVM, neural networks, and cross-conformal prediction algorithms as examples. Have an understanding of ways to apply the ideas and algorithms of machine learning in industry.

Implemented various machine learning algorithms and techniques learned during the course, such as Nearest Neighbours, conformal prediction, linear regression, Ridge Regression, Lasso, data preprocessing, parameter selection, kernels, neural networks, support vector machines, scikit-learn pipelines, and cross-conformal predictors.

An assignment exploring valuation of options using methods like Black-Scholes, binomial trees, and Monte Carlo. Also includes theoretical aspects of put-call parity and financial arbitrage opportunities.

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

A Pacman game where various agents are implemented to play the game effectively.

A collection of Leetcode solutions in Python. This is used to practice algorithms and data structures. They are also used to practice unit testing. CI/CD is also used to run the tests when merging to the main branch.

Jupyter Notebook containing various searching and sorting algorithms. Each algorithms is explained. All the algorithms are also compared to each other.