R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
this chapter, we'll cover the following topics: Reading and writing vector data Creating and manipulating vector data Visualizing (plotting) vector data on a map Working with map projections and reproject data Performing spatial operations such as spatial joins Working with vector geometries and attribute data in tabular form Analyzing the results ... See more
Paul Crickard • Mastering Geospatial Analysis with Python: Explore GIS processing and learn to work with GeoDjango, CARTOframes and MapboxGL-Jupyter
Sometimes we want to analyze clusters of data (K-means) or reduce the dimensions or features in play (manifold learning) or convert from one type of data to another (autoencoding).
Eric Redmond • Deep Tech: Demystifying the Breakthrough Technologies That Will Revolutionize Everything
The second variable is the independent variable (X), which again predicts whether the pizza will be late but in relation to an independent event. In our fictional scenario, ‘day of the week,’ could be an independent variable. Machine learning then boils down to discerning how X (the independent variable/s) affects y (the dependent variable).
Oliver Theobald • Machine Learning for Absolute Beginners: A Plain English Introduction (First Edition)
robust, interactive view of data. Are there ways to navigate through data in an interactive, 3-D environment to gain new insights?