You're reading from Machine Learning with Amazon SageMaker Cookbook 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments

Product type Paperback

Published in Oct 2021

Publisher Packt

ISBN-13 9781800567030

Length 762 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Machine Learning

Author (1):

Joshua Arvin Lat

View More author details

Table of Contents (11) Chapters

Preface

1. Chapter 1: Getting Started with Machine Learning Using Amazon SageMaker

2. Chapter 2: Building and Using Your Own Algorithm Container Image FREE CHAPTER

3. Chapter 3: Using Machine Learning and Deep Learning Frameworks with Amazon SageMaker

4. Chapter 4: Preparing, Processing, and Analyzing the Data

5. Chapter 5: Effectively Managing Machine Learning Experiments

6. Chapter 6: Automated Machine Learning in Amazon SageMaker

7. Chapter 7: Working with SageMaker Feature Store, SageMaker Clarify, and SageMaker Model Monitor

8. Chapter 8: Solving NLP, Image Classification, and Time-Series Forecasting Problems with Built-in Algorithms

9. Chapter 9: Managing Machine Learning Workflows and Deployments

10. Other Books You May Enjoy

Visualizing and understanding your data in Python

In this recipe, we will load the sample dataset and generate a scatter plot to explore the relationship between the variables in the dataset. As you can see in the following screenshot, we have started with a DataFrame containing the management_experience_months and monthly_salary values and generated a visualization that allows us to observe the linear relationship between these two variables:

Figure 1.34 – Using matplotlib to generate a scatter plot chart from a DataFrame

The objective of this recipe is for us to understand the data first using plotting libraries (for example, matplotlib) before diving directly into the other steps of the ML process. We will start by loading a sample dataset from a CSV file to a pandas DataFrame and then use matplotlib to generate a scatter plot.