Book Image

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

By : Somanath Nanda, Weslley Moura
Book Image

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

By: Somanath Nanda, Weslley Moura

Overview of this book

The AWS Certified Machine Learning Specialty exam tests your competency to perform machine learning (ML) on AWS infrastructure. This book covers the entire exam syllabus using practical examples to help you with your real-world machine learning projects on AWS. Starting with an introduction to machine learning on AWS, you'll learn the fundamentals of machine learning and explore important AWS services for artificial intelligence (AI). You'll then see how to prepare data for machine learning and discover a wide variety of techniques for data manipulation and transformation for different types of variables. The book also shows you how to handle missing data and outliers and takes you through various machine learning tasks such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, along with the specific ML algorithms you need to know to pass the exam. Finally, you'll explore model evaluation, optimization, and deployment and get to grips with deploying models in a production environment and monitoring them. By the end of this book, you'll have gained knowledge of the key challenges in machine learning and the solutions that AWS has released for each of them, along with the tools, methods, and techniques commonly used in each domain of AWS ML.
Table of Contents (14 chapters)
Section 1: Introduction to Machine Learning
Section 2: Data Engineering and Exploratory Data Analysis
Section 3: Data Modeling

Visualizing comparisons in your data

Comparisons are very common in data analysis and we have different ways to present them. Let's start with the bar chart. I am sure you have seen many reports that have used this type of visualization.

Bar charts can be used to compare one variable among different classes; for example, a car's price across different models or population size per country. In the following graph, we have used a bar chart to present the number of Covid-19 cases per state in India, until June 2020:

Figure 4.3 – Plotting comparisons with a bar chart

Sometimes, we can also use stacked column charts to add another dimension to the data that is being analyzed. For example, in the following graph, we are using a stacked bar chart to show how many people were on board the Titanic, per gender. Additionally, we are breaking down the number of people who survived (positive class) and those who did not (negative class):