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 text data

We have already learned how to transform categorical features into numerical representations, either using label encoders, ordinal encoders, or one-hot encoding. However, what if we have fields containing long piece of text in our dataset? How are we supposed to provide a mathematical representation for them in order to properly feed ML algorithms? This is a common issue in natural language processing (NLP), a subfield of AI.

NLP models aim to extract knowledge from texts; for example, translating text between languages, identifying entities in a corpus of text (also known as Name Entity Recognition (NER)), classifying sentiments from a user review, and many other applications.

Important note

In Chapter 2, AWS Application Services for AI/ML, you learned about some AWS application services that apply NLP to their solutions, such as Amazon Translate and Amazon Comprehend. During the exam, you might be asked to think about the fastest or easiest way (with...