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

Dealing with outliers

We are not on this studying journey just to pass the AWS Machine Learning Specialty exam, but also to become better data scientists. There are many different ways to look at the outlier problem purely from a mathematical perspective; however, the datasets we use are derived from the underlying business process, so we must include a business perspective during an outlier analysis.

An outlier is an atypical data point in a set of data. For example, the following chart shows some data points that have been plotted in a two-dimension plan; that is, x and y. The red point is an outlier, since it is an atypical value on this series of data:

Figure 3.19 – Identifying an outlier

We want to treat outlier values because some statistical methods are impacted by them. Still, in the preceding chart, we can see this behavior in action. On the left-hand side, we drew a line that best fits those data points, ignoring the red point. On the right...