Data Science
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COVID-19: The Data We Have, and How We Can Use It
This article covers the epidemiology of the coronavirus crisis, what type of actionable data we have, and what can be done with it.
Introduction to Naive Bayes: A Probability-Based Classification Algorithm
Naive Bayes is one of the simplest machine learning algorithms for classification. We'll cover an introduction to Naive Bayes, and implement it in Python.
Gradient Boosting In Classification: Not a Black Box Anymore!
In this article we'll cover how gradient boosting works intuitively and mathematically, its implementation in Python, and pros and cons of its use.
How To Implement Support Vector Machine With Scikit-Learn
Support vector machine is one of the most popular classical machine learning methods. In this tutorial we'll cover SVM and its implementation in Python.
Implementing The Levenshtein Distance for Word Autocompletion and Autocorrection
This tutorial works through a step-by-step example of how to implement the Levenshtein distance in Python for word autocorrection and autocompletion.
Measuring Text Similarity Using the Levenshtein Distance
This tutorial works through a step-by-step example of how the Levenshtein distance is calculated using dynamic programming.