最新章節

• Summary
• Going beyond classification and regression
• And what is the most popular vegetable?
• Removing duplicate tweets
• Sentiment analysis with TextBlob
• Download the dataset from RedShift

• Summary 更新時間：2021-07-03 00:18:24
• Going beyond classification and regression
• And what is the most popular vegetable?
• Removing duplicate tweets
• Sentiment analysis with TextBlob
• Download the dataset from RedShift
• Analyzing the results
• Preprocessing with Lambda
• Debugging
• Data format synchronization
• Dependencies and debugging
• Setting up the roles and policies
• Adding Redshift to the Kinesis Firehose
• The Redshift database
• Producing tweets
• Setting up Kinesis Firehose
• Kinesis Analytics
• Kinesis Stream
• Kinesis
• The training dataset and the model
• Popularity contest on twitter
• Streaming Twitter sentiment analysis
• Building a Streaming Data Analysis Pipeline
• Summary
• Interpreting the results
• Driving the trials in Python
• Polynomial regression in Amazon ML
• Establishing a baseline
• Introducing polynomial regression
• Uploading the nonlinear data to Redshift
• Creating our own non-linear dataset
• Executing Redshift queries using Psql
• Connecting through the command line
• Creating a Redshift instance
• Choosing between RDS and Redshift
• Creating Datasources from Redshift
• Summary
• Managing schema and recipe
• Implementing recursive feature selection with Boto3
• Wrapping up the Python-based workflow
• Waiting on operation completion
• Working with the Python SDK for Amazon Machine Learning
• Boto3 the Python SDK
• Conclusion
• The results
• Generating the evaluations template
• Generating the models template
• Generating the datasources template
• Generating the shuffled datasets
• Implementing Monte Carlo cross-validation
• What is cross-validation?
• Evaluating our model with create-evaluation
• Creating the model
• Creating the datasource
• An overview of Amazon ML CLI commands
• A simple project using the CLI
• Splitting the dataset with shell commands
• Introducing the Ames Housing dataset
• Passing parameters using JSON files
• Picking up CLI syntax
• Installing AWS CLI
• Using the CLI versus SDK
• Getting started and setting up
• Command Line and SDK
• Summary
• Python SDK
• Setting up AWS CLI
• AWS access keys
• Setting up AWS credentials
• AWS SDK
• Setting up real-time predictions
• Manually exploring variable influence
• Making real-time predictions
• Multiplying trials
• Finding out who will survive
• Evaluating the held-out dataset
• Assessing our predictions
• Reading the results file
• Reading the manifest file
• Interpreting prediction outputs
• Creating the batch prediction job
• Making batch predictions
• Predictions and Performances
• Summary
• Parsing the model logs
• Keeping variables as numeric or applying quantile binning?
• Comparing different recipes on the Titanic dataset
• Impact of regularization
• Visualizing convergence
• Optimizing the learning rate
• Analyzing the logs
• Evaluating multiclass classification
• Evaluating linear regression
• Exploring the model performances
• Evaluating binary classification
• Evaluating the model
• Creating an evaluation
• Choosing regularization
• Setting the number of data passes
• Setting model memory
• Parametrizing the model
• Choosing between recipes and data pre-processing.
• Applying recipes to the Titanic dataset
• Editing the suggested recipe
• Creating a model
• Binning numeric values
• Coupling variables
• Text mining
• Using simple transformations
• Data processing through seven transformations
• Specifying outputs
• Naming variables with assignments
• Grouping variables
• Managing variables
• Transforming data with recipes
• Model Creation
• Summary
• Creating an improved datasource
• Wrapping up
• Calculating family size
• Inferring the deck from the cabin
• Extracting the title from the name
• Handling outliers in the fare
• Missing values
• Data munging in SQL
• Creating the database and table directly in SQL
• Using the wizard
• Creating a titanic database
• A brief tour of AWS Athena
• Introducing Athena
• Feature engineering with Athena
• Examining data statistics
• Reusing the schema
• Verifying the data schema
• Creating the datasource
• Formatting the data
• Granting permissions
• Loading the data
• Creating a bucket
• Loading data on S3
• Splitting the data
• Preparing the data
• Introducing the Titanic dataset
• Finding open datasets
• Working with datasets
• Loading and Preparing the Dataset
• Summary
• Making batch predictions
• Comparing with a baseline
• The evaluation of the model
• The model
• Creating the datasource
• Declaring a datasource
• Loading the data on S3
• The dataset
• Overview of a standard Amazon Machine Learning workflow
• Choosing a region
• Creating login credentials
• Defining policies
• Creating a user
• Setting up the account
• Security
• Opening an Amazon Web Services Account
• Overview of an Amazon Machine Learning Workflow
• Summary
• Evaluating the performance of your model
• L1 regularization and Lasso
• L2 regularization and Ridge
• Regularization on linear models
• Overfitting
• Underfitting
• Identifying and correcting poor performances
• Training and evaluation in Amazon ML
• The predictive analytics workflow
• Preprocessing recapitulation
• Adding features?
• Accepting non-linear patterns
• Detecting outliers
• Addressing multicollinearity
• Imbalanced datasets
• Normalization
• Missing values
• Assumptions for multiclass linear models
• Classic datasets versus real-world datasets
• Dealing with messy data
• What's an algorithm? What's a model?
• Machine Learning Definitions and Concepts
• Summary
• The statistical approach versus the machine learning approach
• Missing from Amazon ML
• Validating the dataset
• Diving further into linear modeling for prediction
• Extracting features to predict outcomes
• Expanding regression to classification with logistic regression
• Regression versus classification
• Building the simplest predictive analytics algorithm
• Understanding predictive analytics
• Pricing
• Amazon's expertise and the gradient descent algorithm
• Engineering data versus model variety
• Comparing performances
• Leveraging full AWS integration
• Machine Learning as a Service
• Introducing Amazon Machine Learning
• Introduction to Machine Learning and Predictive Analytics
• Questions
• Piracy
• Errata
• Downloading the example code
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• Dedication
• Customer Feedback
• www.PacktPub.com
• About the Reviewer
• About the Author
• Credits
• 版權信息
• 封面

• 封面
• 版權信息
• 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
• Summary
• 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
• Summary
• 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
• Creating the datasource
• 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
• Missing values
• Handling outliers in the fare
• Extracting the title from the name
• Inferring the deck from the cabin
• Calculating family size
• Wrapping up
• Creating an improved datasource
• Summary
• Model Creation
• Transforming data with recipes
• Managing variables
• Grouping variables
• Naming variables with assignments
• Specifying outputs
• Data processing through seven transformations
• Using simple transformations
• Text mining
• Coupling variables
• Binning numeric values
• Creating a model
• Editing the suggested recipe
• Applying recipes to the Titanic dataset
• Choosing between recipes and data pre-processing.
• Parametrizing the model
• Setting model memory
• Setting the number of data passes
• Choosing regularization
• Creating an evaluation
• Evaluating the model
• Evaluating binary classification
• Exploring the model performances
• Evaluating linear regression
• Evaluating multiclass classification
• Analyzing the logs
• Optimizing the learning rate
• Visualizing convergence
• Impact of regularization
• Comparing different recipes on the Titanic dataset
• Keeping variables as numeric or applying quantile binning?
• Parsing the model logs
• Summary
• Predictions and Performances
• Making batch predictions
• Creating the batch prediction job
• Interpreting prediction outputs
• Reading the manifest file
• Reading the results file
• Assessing our predictions
• Evaluating the held-out dataset
• Finding out who will survive
• Multiplying trials
• Making real-time predictions
• Manually exploring variable influence
• Setting up real-time predictions
• AWS SDK
• Setting up AWS credentials
• AWS access keys
• Setting up AWS CLI
• Python SDK
• Summary
• Command Line and SDK
• Getting started and setting up
• Using the CLI versus SDK
• Installing AWS CLI
• Picking up CLI syntax
• Passing parameters using JSON files
• Introducing the Ames Housing dataset
• Splitting the dataset with shell commands
• A simple project using the CLI
• An overview of Amazon ML CLI commands
• Creating the datasource
• Creating the model
• Evaluating our model with create-evaluation
• What is cross-validation?
• Implementing Monte Carlo cross-validation
• Generating the shuffled datasets
• Generating the datasources template
• Generating the models template
• Generating the evaluations template
• The results
• Conclusion
• Boto3 the Python SDK
• Working with the Python SDK for Amazon Machine Learning
• Waiting on operation completion
• Wrapping up the Python-based workflow
• Implementing recursive feature selection with Boto3
• Managing schema and recipe
• Summary
• Creating Datasources from Redshift
• Choosing between RDS and Redshift
• Creating a Redshift instance
• Connecting through the command line
• Executing Redshift queries using Psql
• Creating our own non-linear dataset
• Uploading the nonlinear data to Redshift
• Introducing polynomial regression
• Establishing a baseline
• Polynomial regression in Amazon ML
• Driving the trials in Python
• Interpreting the results
• Summary
• Building a Streaming Data Analysis Pipeline
• Streaming Twitter sentiment analysis
• Popularity contest on twitter
• The training dataset and the model
• Kinesis
• Kinesis Stream
• Kinesis Analytics
• Setting up Kinesis Firehose
• Producing tweets
• The Redshift database
• Adding Redshift to the Kinesis Firehose
• Setting up the roles and policies
• Dependencies and debugging
• Data format synchronization
• Debugging
• Preprocessing with Lambda
• Analyzing the results
• Download the dataset from RedShift
• Sentiment analysis with TextBlob
• Removing duplicate tweets
• And what is the most popular vegetable?
• Going beyond classification and regression
• Summary 更新時間：2021-07-03 00:18:24