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 unbalanced datasets

At this point, I hope you have realized why data preparation is probably the longest part of our work. We have learned about data transformation, missing data values, and outliers, but the list of problems goes on. Don't worry – bear with me and let's master this topic together!

Another well-known problem with ML models, specifically with binary classification problems, is unbalanced classes. In a binary classification model, we say that a dataset is unbalanced when most of its observations belong to the same class (target variable).

This is very common in fraud identification systems, for example, where most of the events belong to a regular operation, while a very small number of events belong to a fraudulent operation. In this case, we can also say that fraud is a rare event.

There is no strong rule for defining whether a dataset is unbalanced or not, in the sense of it being necessary to worry about it. Most challenge problems...