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

Model optimization

As you know, understanding evaluation metrics is very important in order to measure your model's performance and document your work. In the same way, when we want to optimize our current models, evaluating metrics also plays a very important role in defining the baseline performance that we want to challenge.

The process of model optimization consists of finding the best configuration (also known as hyperparameters) of the machine learning algorithm for a particular data distribution. We don't want to find hyperparameters that overfit the training data in the same way that we don't want to find hyperparameters that underfit the training data.

You learned about overfitting and underfitting in Chapter 1, Machine Learning Fundamentals. In the same chapter, you also learned how to avoid these two types of modeling issues.

In this section, we will learn about some techniques that you can use to find the best configuration for a particular algorithm...