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

Evaluating regression models

Regression models are quite different from classification models since the outcome of the model is a continuous number. Therefore, the metrics around regression models aim to monitor the difference between real and predicted values.

The simplest way to check the difference between a predicted value (yhat) and its actual value (y) is by performing a simple subtraction operation, where the error will be equal to the absolute value of yhat – y. This metric is known as the Mean Absolute Error (MAE).

Since we usually have to evaluate the error of each prediction, i, we have to take the mean value of the errors. The following formula shows how this error can be formally defined:

Sometimes, you might want to penalize bigger errors over smaller errors. To achieve this, you can use another metric, which is known as the Mean Squared Error (MSE). MSE will square each error and return the mean value.

By squaring errors,...