- What is the main difference between random forests and gradient-boosted trees?
- Explain why the Gini Impurity may be interpreted as the misclassification rate.
- Explain why it is necessary to perform feature encoding for categorical features.
- In this chapter, we provided two ways to do feature encoding. Find one other way to encode categorical features.
- Explain why the accuracy metric we used in Chapter 2, Classifying Twitter Feeds with Naive Bayes, is not suitable for predicting clicks on our dataset.
- Find other objectives we can use for the XGBoost algorithm. When would you use each objective?
Mastering Machine Learning on AWS
By :
Mastering Machine Learning on AWS
By:
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.
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Current Title:
Mastering Machine Learning on AWS
Table of Contents (24 chapters)
Preface
Getting Started with Machine Learning for AWS
Section 2: Implementing Machine Learning Algorithms at Scale on AWS
Classifying Twitter Feeds with Naive Bayes
Predicting House Value with Regression Algorithms
Predicting User Behavior with Tree-Based Methods
Customer Segmentation Using Clustering Algorithms
Analyzing Visitor Patterns to Make Recommendations
Section 3: Deep Learning
Implementing Deep Learning Algorithms
Implementing Deep Learning with TensorFlow on AWS
Image Classification and Detection with SageMaker
Section 4: Integrating Ready-Made AWS Machine Learning Services
Working with AWS Comprehend
Using AWS Rekognition
Building Conversational Interfaces Using AWS Lex
Section 5: Optimizing and Deploying Models through AWS
Creating Clusters on AWS
Optimizing Models in Spark and SageMaker
Tuning Clusters for Machine Learning
Deploying Models Built in AWS
Other Books You May Enjoy
Appendix: Getting Started with AWS
Customer Reviews