I am co-founder at Keatser Inc. I love things related to Deep Learning and Data Science. My research interest focuses on Computer Vision, Deep Neural networks and few fields of Cognitive Science.
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.
In this article we'll cover how gradient boosting works intuitively and mathematically, its implementation in Python, and pros and cons of its use.
AdaBoost is a very popular boosting technique. Here we'll cover the AdaBoost algorithm, its pros and cons, and implement it in Python using scikit-learn.
In this guide we'll cover random forests, one of the most popular machine learning algorithms, and see how to implement them in Python.
This is a 2020 guide to decision trees, which are foundational to many machine learning algorithms including random forests and various ensemble methods.
Learn how bagging and ensemble methods decrease variance and prevent overfitting in this 2020 guide to bagging, including an implementation in Python.
In part two of deploying deep learning models, learn how to host on Paperspace.
In this tutorial we'll see how you can take your work and give it an audience by deploying your projects on the web
