In the first and second course of the “Machine Learning for Absolute Beginners” training program, you have learned the fundamentals of AI and machine learning and discovered methods to pre-process the data before moving it into the machine learning algorithms. In this third and final course of the program, you will learn how to create eye-catching data visualizations using Python, Seaborn, and Matplotlib.
The course starts by highlighting the learning objectives and then takes you through the fundamentals of Matplotlib and Seaborn. You will learn how to use figures, axes, customization techniques, and NumPy to perform data visualization. In the rest of this course, you will discover how to develop the ranking, proportion, trend, distribution, and correlation charts.
By the end of this course, you will have the knowledge and skills to perform data visualization and exploratory data analysis (EDA) using Python, Matplotlib, and Seaborn.
The code files and all related files are placed on GitHub at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginn…
Become familiar with object-oriented and Pyplot interfaces of Matplotlib
Understand Seaborn and figure-level and axes-level functions
Find out how to create pie, treemap, and swarm charts
Plot histogram, density, box, and whisker charts
Create bar, grouped bar, stacked bar, and lollipop charts
Create scatter, correlogram, line, and area charts