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 compositions in your data

Sometimes, you want to analyze the various elements that compose your feature; for example, the percentage of sales per region or percentage of queries per channel. In both examples, we are not considering any time dimension; instead, we are just looking at the data as a whole. For these types of compositions, where you don't have the time dimension, you could show your data using pie charts, stacked 100% bar charts, and treemaps.

The following is a pie chart showing the number of queries per customer channel, for a given company, during a pre-defined period of time:

Figure 4.11 – Plotting compositions with a pie chart

If you want to show compositions while considering a time dimension, then your most common options would be a stacked area chart, a stacked 100% area chart, a stacked column chart, or a stacked 100% column chart. For reference, take a look at the following chart, which shows the sales per region...