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

Summary

In this chapter, you learned about the main metrics for model evaluation. We first started with the metrics for classification problems and then we moved on to the metrics for regression problems.

In terms of classification metrics, you have been introduced to the well-known confusion matrix, which is probably the most important artifact to perform a model evaluation on classification models.

Aside from knowing what true positive, true negative, false positive, and false negative are, we have learned how to combine these components to extract other metrics, such as accuracy, precision, recall, the F1 score, and AUC.

We went even deeper and learned about ROC curves, as well as precision-recall curves. We learned that we can use ROC curves to evaluate fairly balanced datasets and precision-recall curves for moderate to imbalanced datasets.

By the way, when you are dealing with imbalanced datasets, remember that using accuracy might not be a good idea.

In terms...