Machine Learning for Absolute Beginners - Level 3

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In this course, you will learn the fundamentals of data visualization in Python using the well-known Matplotlib and Seaborn data science libraries and perform exploratory data analysis (EDA) by visualizing a data set using a variety of charts.

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44 on-demand videos & exercises
Level: All Levels
English
2hrs 59mins
Access on mobile, web and TV

What to know about this course

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-Beginners---Level-3

Who's this course for?

This course is designed for absolute beginners who want to learn how to visualize and create appealing charts using Python, Matplotlib, and Seaborn. Basic Python skills and good knowledge of the Pandas library is needed to get started with this course. Hence, it is recommended to complete Level 1 and Level 2 of the "Machine Learning for Absolute Beginners" training program before taking up this course.

What you'll learn

  • 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.

Key Features

  • Grasp the fundamentals of Matplotlib and Seaborn.
  • Become confident in performing exploratory data analysis (EDA) for any data sets.
  • Get ready to visualize data using a variety of charts.

Course Curriculum

About the Author

Idan Gabrieli

Idan Gabrieli has worked in various engineering positions in Israel’s high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies. Idan’s expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers. Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options. Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies. Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers. Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.

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