A covariance matrix is a square matrix that shows the covariance between every pair of variables in a given data set, where each element in the matrix represents the corresponding covariance.
As a data scientist, developing great models and extrapolating nuanced insights won’t get you far if you can’t communicate your findings clearly. Here’s how to present your work using bokeh.
Mahalanobis distance is a distance metric that finds the distance between a point and a distribution. It’s often used for detecting outliers in multivariate data.
Correlation occurs when two variables change at the same time, while causation is when a change in one variable causes the other to change. Here’s why you need to understand the difference.
A NumPy random seed is a numerical value in Python that initializes a random number generator, allowing for reproducible results. Here's why to use np.random.default_rng() instead to set random seeds for individual functions/classes in Python.
K-nearest neighbor (KNN) is an algorithm that is used to classify a data point based on how its neighbors are classified. Here’s what you need to know.
In this four-part series, I’ll show you how the Markov decision process works and the mathematical theory you need to know to understand deep reinforcement learning.
