Python

I began my coding journey taking university courses called Intro and Intermediate Programming in Python. Then I took a very interesting course in Big Data Economics, using Python to perform different sorts of machine learning such as Neural Networks, classification, regression, dimensionality reduction, etc. I continued to educate myself about these topics through DataCamp modules and courses. In the meantime, I’ve learned other programming languages such as R, Java, SAS and Stata. As an undergraduate summer researcher, I used Python to replicate certain experiments statistics literature for testing for neglected nonlinearity in multivariate time series such as ARIMA and Markov-Switching models. This experience made me want to pursue a statistics minor which allowed me to take more advanced courses in statistics, machine learning, and programming. I then enrolled in a Deep Learning course where we are using Python libraries such as PyTorch create Convolutional Neural Networks to do image processing on MNIST dataset. In this course I’ve learned about sequence models (RNNs), reinforcement learning (q-learning, markov-decision process), and GenAI (discriminator/generator models). Additionally, in a course centered on statistical Learning and inference, I gained comfort with Bayesian Methods, MLE, and different optimization algorithms such as optimizers (SGD, Adam, SLSQP) in Python. Shortly after graduation, I’ve completed the deep learning specialization offered by Andrew Ng and DeepLearning.AI, where I’ve gotten more exposure in the world of computer vision (object detection, edge detection), NLP (transformers, attention, machine translation, word representations), transfer learning as well as solidifying my fundamentals in the world of ML, DL and NNs.

Projects:

Webscraping Music Data intro using Requests, BeautifulSoup

Mathematical and Statistical Applications

Home Price Estimation using Ensemble Regression Model

Music Genre Classification using GTZAN dataset