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NumPy (oftentimes also written as Numpy) is a linear algebra library, which is the basis for most of the scientific computing done in Python. Most libraries for data science, analysis and machine learning build on top of NumPy as the basis for linear algebra, and also have tight coupling and...
Felipe Antunes
Article
Sometimes confused with linear regression by novices - due to sharing the term regression - logistic regression is far different from linear regression. While linear regression predicts values such as 2, 2.45, 6.77 or continuous values, making it a regression algorithm, logistic regression predicts values such as 0...
Cássia Sampaio
Missing values are common and occur either due to human error, instrument error, processing from another team, or otherwise just a lack of data for a certain observation. In this Byte, we'll take a look at how to fill NaNs in a DataFrame, if you choose to handle NaNs by...
David Landup
In this guide - we'll take a look at how to calculate the Euclidean distance between two points in Python, using Numpy. Euclidean distance is a fundamental distance metric pertaining to systems in Euclidean space. Euclidean space is the classical geometrical space you get familiar with in Math class, typically...
Bilal Hamada
Numpy is the most popular mathematical computing Python library. It offers a great number of mathematical tools including but not limited to multi-dimensional arrays and matrices, mathematical functions, number generators, and a lot more. One of the fundamental tools in NumPy is the ndarray - an N-dimensional array. Today, we're...
This guide is an in-depth introduction to an unsupervised dimensionality reduction technique called Random Projections. A Random Projection can be used to reduce the complexity and size of data, making the data easier to process and visualize. It is also a preprocessing technique for input preparation to a classifier or...
Mehreen Saeed
In this guide, we'll be taking a look at an unsupervised learning model, known as a Self-Organizing Map (SOM), as well as its implementation in Python. We'll be using an RGB Color example to train the SOM and demonstrate its performance and typical usage. Self-Organizing Maps: A General Introduction A...
This guide is an introduction to Spearman's rank correlation coefficient, its mathematical calculation, and its computation via Python's pandas library. We'll construct various examples to gain a basic understanding of this coefficient and demonstrate how to visualize the correlation matrix via heatmaps. What Is the Spearman Rank Correlation Coefficient? Spearman...
There are many data visualization libraries in Python, yet Matplotlib is the most popular library out of all of them. Matplotlib’s popularity is due to its reliability and utility - it's able to create both simple and complex plots with little code. You can also customize the plots in...
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