更新时间：2021-07-03 00:18:24
封面
版权信息
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Dedication
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
Introduction to Machine Learning and Predictive Analytics
Introducing Amazon Machine Learning
Machine Learning as a Service
Leveraging full AWS integration
Comparing performances
Engineering data versus model variety
Amazon's expertise and the gradient descent algorithm
Pricing
Understanding predictive analytics
Building the simplest predictive analytics algorithm
Regression versus classification
Expanding regression to classification with logistic regression
Extracting features to predict outcomes
Diving further into linear modeling for prediction
Validating the dataset
Missing from Amazon ML
The statistical approach versus the machine learning approach
Summary
Machine Learning Definitions and Concepts
What's an algorithm? What's a model?
Dealing with messy data
Classic datasets versus real-world datasets
Assumptions for multiclass linear models
Missing values
Normalization
Imbalanced datasets
Addressing multicollinearity
Detecting outliers
Accepting non-linear patterns
Adding features?
Preprocessing recapitulation
The predictive analytics workflow
Training and evaluation in Amazon ML
Identifying and correcting poor performances
Underfitting
Overfitting
Regularization on linear models
L2 regularization and Ridge
L1 regularization and Lasso
Evaluating the performance of your model
Overview of an Amazon Machine Learning Workflow
Opening an Amazon Web Services Account
Security
Setting up the account
Creating a user
Defining policies
Creating login credentials
Choosing a region
Overview of a standard Amazon Machine Learning workflow
The dataset
Loading the data on S3
Declaring a datasource
Creating the datasource
The model
The evaluation of the model
Comparing with a baseline
Making batch predictions
Loading and Preparing the Dataset
Working with datasets
Finding open datasets
Introducing the Titanic dataset
Preparing the data
Splitting the data
Loading data on S3
Creating a bucket
Loading the data
Granting permissions
Formatting the data
Verifying the data schema
Reusing the schema
Examining data statistics
Feature engineering with Athena
Introducing Athena
A brief tour of AWS Athena
Creating a titanic database
Using the wizard
Creating the database and table directly in SQL
Data munging in SQL
