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NumPy Essential Training: 2 MatPlotlib and Linear Algebra Capabilities

NumPy Essential Training: 2 MatPlotlib and Linear Algebra Capabilities

1h 13mIntermediate2022-03-04

Authors

Terezija Semenski

Terezija Semenski

Software Developer, Mathematician, Writer, and Learner

Course details

NumPy provides the numerical backend for nearly every scientific or technical library for Python. Many advanced data science and machine learning libraries require data to be in the form of NumPy arrays before it can be processed. In this course, instructor Terezija Semenski gives you a closer look at advanced features in NumPy and Matplotlib. Matplotlib is the most popular library for plotting with NumPy. Terezija steps you through the basics of plotting functions and implementing figures with Matplotlib, then goes over advanced commands and plots. She introduces universal functions in NumPy, as well as strides, structure arrays, dates, and times. Terezija also covers basic linear algebra capabilities that you can apply in NumPy, including decomposition, polynomial mathematics, and linear regression.

Skills covered

NumPyData VisualizationMachine LearningData AnalysisEssential TrainingArtificial Intelligence (AI)Data ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen Source

Concepts

0. Introduction

  • 01 - Introduction
  • 02 - What you should know

1. Plotting with Matplotlib

  • 03 - Why should you use Matplotlib
  • 04 - Matplotlib basics
  • 05 - Understanding figures
  • 06 - Matplotlib subplots functionality
  • 07 - Understanding legends
  • 08 - Challenge - Implementing a figure
  • 09 - Solution - Implementing a figure

2. Matplotlib Styling and Advanced Plots

  • 10 - Colors and styles
  • 11 - Advanced Matplotlib commands
  • 12 - Adding annotations
  • 13 - Creating pie charts and bar charts
  • 14 - Advanced plots

3. From Beginner to Advanced NumPy

  • 15 - Universal functions
  • 16 - Introducing strides
  • 17 - Structured arrays
  • 18 - Dates and time in NumPy

4. Linear Algebra in NumPy

  • 19 - Linear algebra capabilities in NumPy
  • 20 - Decomposition
  • 21 - Polynomial mathematics
  • 22 - Application - Linear regression

Conclusion

  • 23 - Next steps

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