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dsaber.com
| | www.pythoncharts.com
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| | A tutorial on creating a line chart with confidence intervals in Python using Matplotlib, Seaborn, Altair and Plotly, including interactive versions.
| | www.analyticsvidhya.com
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| | Histogram in Python using Matplotlib: Explore when to use them, loading data, plotting with Matplotlib, and optimizing styles for clarity.
| | matbesancon.xyz
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| | Learning by doing: detecting fraud on bank notes using Python in 3 steps.
| | www.jeremykun.com
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| The singular value decomposition (SVD) of a matrix is a fundamental tool in computer science, data analysis, and statistics. It's used for all kinds of applications from regression to prediction, to finding approximate solutions to optimization problems. In this series of two posts we'll motivate, define, compute, and use the singular value decomposition to analyze some data. (Jump to the second post) I want to spend the first post entirely on motivation and background.