My enthusiasm lies within the realm of Data Science, built upon a solid base of mathematics, economics, and machine learning. At present, I am studying an MSc. in Statistics and have graduated from studying a BSc. in Economics and Mathematics. Personally, I enjoy utilising and leveraging data-driven solutions in order to solve complex problems, and telling stories through data.
View my portfolio here.
This page a collection is a collection of my selected data science related projects, used to explore components of machine learning.
At the moment, from a more theoretical perspective, I am interested in the applications of Transformer architecture. As such, a few of my projects are exploring applications them. The versatile and exciting applications in NLP and Computer Vision are what I find interesting. You can find my CV/resume here.
I am currently developing an educational web app on transformers. If you would like to contact me about this please send me an email.
Explored movie review sentiment classification problem using LSTM Recurrent Neural Network, with Keras.
Neural network for basic multi - class classification.
A paper implementation of the (original) Vision Transformer (ViT) architecture using PyTorch. Applies convolutional neural network (CNN) method. It follows from code by Daniel Bourke
Report and Presentation
Two studies applying Random Forest using R:
- Classifying patient heart conditions from ECG data (classification) and 2. Building train delay prediction model (regression). This was joint work with Weiyun Wu, Alastair Harrison and Ying Zhan.
Report and Presentation
A study on background, performance and evaluation of Support Vector Machines in solving classification problems (in Python), compared with other classification methods. This was a collaboration with Jake Dorman, Anas Almhmadi and Rishabh Agarwal.
A project exploring text summarisation, applying tokenisation and extraction method.
Implementation of a collaborative filtering (CF) based system looking at user - based and item based CF and Alternating Least Squares (ALS) on a restaurant problem.
I tend to learn better when trying to apply concepts from papers. Below are some basic paper implementations directly using labml and most of their implementation code:
Programming skills: Python (Base, Pandas, NumPy, Matplotlib, Scikit-Learn, PySpark, PyTorch), R, SQL, VBA
Machine Learning skills: TensorFlow, SVM, Decision Trees, Random Forest, Gradient Descent, KNN, PCA
If you have any questions or would like to collaborate on a project, don't hesitate to get in touch! Please contact:
- Email: [email protected]
- LinkedIn: linkedin.com/in/timifolaranmi/