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

Predicting User Behavior with Tree-Based Methods

This chapter will introduce decision trees, random forests, and gradient-boosted trees. The use of decision trees is popular in data science because they provide a visual representation of how the information in the training set can be represented as a hierarchy. Traversing the hierarchy based on an observation helps you to predict the probability of that event. We will explore how these algorithms can be used to predict when a user may click on online advertisement based on existing advertising click records. Additionally, we will show how to use AWS Elastic MapReduce (EMR) with Apache Spark and the SageMaker XGBoost service to engineer models in the context of big data.

In this chapter, we will cover the following topics:

  • Understanding decision trees
  • Understanding random forests algorithms
  • Understanding gradient boosting algorithms...