If you want to determine the optimal number of clusters in your analysis, you’re faced with an overwhelming number of (mostly subjective) choices. Note that… Read More »Determining Number of Clusters in One Picture
Naive Bayes is a deceptively simple way to find answers to probability questions that involve many inputs. For example, if you’re a website owner, you… Read More »Naive Bayes in One Picture
Ensemble methods take several machine learning techniques and combine them into one predictive model. It is a two step process: Generate the Base Learners: Choose… Read More »Ensemble Methods in One Picture
Logistic regression is regressing data to a line (i.e. finding an average of sorts) so you can fit data to a particular equation and make… Read More »Logistic Regression in One Picture
This is a simple overview of the k-NN process. Perhaps the most challenging step is finding a k that’s “just right”. The square root of n… Read More »K-nn Clustering Explained in One Picture
Determining sample sizes is a challenging undertaking. For simplicity, I’ve limited this picture to the one of the most common testing situation: testing for differences… Read More »Determining Sample Size in One Picture
A non-technical look at A/B testing, based on Dan Siroker & Pete Koomen’s book, A / B Testing, The Most Powerful Way to Turn Clicks… Read More »A/B testing in One Picture
Whether you can call yourself a data scientist if you can’t code is as hotly debated as Brexit. Type the question “Can you be a… Read More »Can you be a Data Scientist without coding?
Grab a copy of The Elements of Statistical Learning (“the machine learning bible”) and you might be a little overwhelmed by the mathematics. For example, this… Read More »Math You Don't Need to Know for Machine Learning
In the nascent field of Data Science, myths are abound. Here’s my top 10, scoured from the internet (where better than to find a myth… Read More »10 Myths About Data Scientists
