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)
1
Section 1: Introduction to Machine Learning
4
Section 2: Data Engineering and Exploratory Data Analysis
9
Section 3: Data Modeling

Dealing with categorical features

Data transformation methods for categorical features will vary according to the sub-type of your variable. In the upcoming sections, we will understand how to transform nominal and ordinal features.

Transforming nominal features

You may have to create numerical representations of your categorical features before applying ML algorithms to them. Some libraries may have embedded logic to handle that transformation for you, but most of them do not.

The first transformation we will cover is known as label encoding. A label encoder is suitable for categorical/nominal variables and it will just associate a number with each distinct label of your variable. The following table shows how a label encoder works:

Figure 3.3 – Label encoder in action

A label encoder will always ensure that a unique number is associated with each distinct label. In the preceding table, although "India" appears twice, the same number...