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Mastering Machine Learning on AWS

Mastering Machine Learning on AWS

By : Dr. Saket S.R. Mengle , Maximo Gurmendez
4.3 (8)
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Mastering Machine Learning on AWS

Mastering Machine Learning on AWS

4.3 (8)
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)
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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

Summary

In this chapter, we covered the basic theoretical concepts for understanding tree ensembles and showed ways to train and evaluate these models in EMR, through Apache Spark, as well as through the SageMaker XGBoost service. Decision tree ensembles are one of the most popular classifiers, for many reasons:

  • They are able to find complex patterns in relatively short training time and with few resources. The XGBoost library is known as the most popular classifier among Kaggle competition winners (these are competitions held to find the best model for an open dataset).
  • It's possible to understand why the classifier is predicting a given value. Following the decision tree paths or just looking at the feature importance are quick ways to understand the rationale behind the decisions made by tree ensembles.
  • Implementations of distributed training are available through Apache...

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