Kevil Khadka's Projects
The Neural Network is one of the most powerful learning algorithms (when a linear classifier doesn't work, this is what I usually turn to), and explaining the 'backpropagation' algorithm for training these models.
Here, we implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, we go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance.
We use support vector machines (SVMs) with various example 2D datasets. Experimenting with these datasets will help us gain an intuition of how SVMs work and how to use a Gaussian kernel with SVMs. In the next half of the exercise, we use support vector machines to build a spam classifier.
Machine Learning: Group Project
Predicting Customer Lifetime Value
Build a predictive model using Azure ML Studio. Demonstrate a working knowledge of setting up experiments on Azure ML Studio. Operationalize machine learning workflows with Azure's drag-and-drop modules.
Intro Statistics with R
Experimental Design
Linear Models/ Linear Regression
Machine Learning (statistical learning)
Techniques for Large Data Sets
Stock Price Prediction of APPLE Using Python
Work on Tableau