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

Handling missing values

As the name suggests, missing values refer to the absence of data. Such absences are usually represented by tokens, which may or may not be implemented in a standard way.

Although using tokens is standard, the way those tokens are displayed may vary across different platforms. For example, relational databases represent missing data with NULL, core Python code will use None, and some Python libraries will represent missing numbers as (Not a Number (NaN).

Important note

For numerical fields, don't replace those standard missing tokens with zeros. By default, zero is not a missing value, but another number. I said "by default" because, in data science, we may face some data quality issues, which we will cover next.

However, in real business scenarios, you may or may not find those standard tokens. For example, a software engineering team might have designed the system to automatically fill missing data with specific tokens, such as ...