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Machine Learning with Amazon SageMaker Cookbook

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

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781800567030
Length 762 pages
Edition 1st Edition
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Author (1):
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Joshua Arvin Lat Joshua Arvin Lat
Author Profile Icon Joshua Arvin Lat
Joshua Arvin Lat
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Table of Contents (11) Chapters Close

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.

Getting ready

This recipe continues on from the Preparing the Amazon S3 bucket and the training dataset for the linear...

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