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Machine Learning for Absolute Beginners - Level 3

Machine Learning for Absolute Beginners - Level 3

  • 30 Day Money Back Guarantee
  • Completion Certificate
  • 24/7 Technical Support

Highlights

  • On-Demand course

  • 2 hours 59 minutes

  • All levels

Description

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.

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

What You Will 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

Audience

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.

Approach

With the help of exercises and activities, this course will show you how to use Python, Matplotlib, and Seaborn for data visualization and exploratory data analysis (EDA).

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

Github Repo

https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-3

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.

Course Outline

1. Getting Started with Level 3!

2. Data Visualization with Matplotlib and Seaborn

3. Ranking and Proportion Charts

4. Trend and Distribution Charts

5. Correlation Charts

6. Course Summary

Course Content

  1. Machine Learning for Absolute Beginners - Level 3

About The Provider

Packt
Packt
Birmingham
Founded in 2004 in Birmingham, UK, Packt’s mission is to help the world put software to work in new ways, through the delivery of effective learning and i...
Read more about Packt

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