Applying Machine Learning and Deep Learning to solve the Knowledge Tracing problem in the context of Programming classrooms. Continue reading Case study: Machine Learning and Deep Learning for Knowledge Tracing in Programming Education
Let us discuss the state-of-the-art methods for transforming every kind of input data into fixed-length vectors of continuous values, including Word2Vec, Doc2Vec, Image2Vec, Node2Vec, Edge2Vec, Code2Vec, and Data2Vec. Continue reading Transforming everything to vectors with Deep Learning: from Word2Vec, Node2Vec, to Code2Vec and Data2Vec
A summary of popular methods to analyze the dependency between variables. Continue reading A survey of correlation analysis methods
This article attempts to make PCA crystal clear to anyone who wishes to understand it thoroughly, step-by-step, in both high and low-level concepts. Continue reading Principal Component Analysis fully explained
A dummy variable is a variable (or feature, predictor, column) whose values can be either 0 or 1. Continue reading When to add a dummy variable?
In Machine Learning, while some predictive models allow categorical variables in the data, most require all predictor variables to be continuous Continue reading How to convert Categorical Variables to Numerical Variables
Many people have a tendency to always do feature centering, scaling or normalizing right before applying predictive models to the data… Continue reading When to do feature centering, scaling and normalization?
Feature selection is hard but very important. Continue reading Feature selection with sklearn
This blog post attempts to address why NaNs are bad and how we can fix them. Continue reading How to deal with missing values (NaNs)