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

Chapter 8: Evaluating and Optimizing Models

It is now time to learn how to evaluate and optimize machine learning models. During the process of modeling, or even after model completion, you might want to understand how your model is performing. Each type of model has its own set of metrics that can be used to evaluate performance, and that is what we are going to study in this chapter.

Apart from model evaluation, as a data scientist, you might also need to improve your model's performance by tuning the hyperparameters of your algorithm. We will take a look at some nuances of this modeling task.

In this chapter, we will cover the following topics:

  • Introducing model evaluation
  • Evaluating classification models
  • Evaluating regression models
  • Model optimization

Alright, let's do it!