Book Image

Mastering Machine Learning on AWS

By : Dr. Saket S.R. Mengle, Maximo Gurmendez
Book Image

Mastering Machine Learning on AWS

By: Dr. Saket S.R. Mengle, Maximo Gurmendez

Overview of this book

Amazon Web Services (AWS) is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned, and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics and predictive modeling through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS.
Table of Contents (24 chapters)
Free Chapter
1
Section 1: Machine Learning on AWS
3
Section 2: Implementing Machine Learning Algorithms at Scale on AWS
9
Section 3: Deep Learning
13
Section 4: Integrating Ready-Made AWS Machine Learning Services
17
Section 5: Optimizing and Deploying Models through AWS
Appendix: Getting Started with AWS

Evaluating regression models

Unlike the Naive Bayes classification model, the regression model provides a numerical output as a prediction. This output can be used for binary classification by predicting the value for both the events and using the maximum value. However, in examples such as predicting a house value based on regressors, we cannot use evaluation metrics that rely on just predicting whether we got the answer correct or incorrect. When we are predicting a numerical value, the evaluation metrics should also quantify the value of error in prediction. For example, if the house value is 600,000 and model A predicts it as 700,000 and model B predicts it as 1,000,000, metrics such as precision and recall will count both these predictions as false positives. However, for regression models, we need evaluation metrics that can tell us that model A was closer to the actual...