This is part of my answer to interview question 9 which is to explain your favorite machine learning algorithm in five minutes.
Bagging & Boosting Made Simple
Bagging and boosting are two different types of ensemble learners. Ensemble learning is a method of combining many weak learners together to build a more complex learner. This is also called ‘meta-learner’ because ensemble learners combine other types of learners to get a final output. A weak learner is simply any learner that does better than random chance.
Continue reading ML Algorithms Made Simple – Bagging & Boosting
Finding and Handling Outliers
An outlier is simply a data point that is far away (smaller or bigger) than the majority of other points. In this post, I will discuss ways to find the outliers in your data, what effect outliers can have on your results, and how to handle the outliers that you do find.
Continue reading Finding and Handling Outliers
Machine learning, you have heard that term by now, but what does it actually mean? What is machine learning? How does a machine actually learn something? What can you do with machine learning?
This post will help you get a basic understanding of what machine learning is and how it works. I will explain the various ways it is currently used and describe the basic types of machine learning algorithms.
Continue reading Machine Learning Basics – A Quick Guide