
Machine Learning in the AWS Cloud
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Content
- Cover
- Title Page
- Copyright
- Acknowledgments
- About the Author
- About the Technical Editor
- Contents at a Glance
- Contents
- Introduction
- Who This Book Is For
- What This Book Covers
- How This Book Is Structured
- What You Need to Use This Book
- Conventions
- Source Code
- Errata
- Part 1 Fundamentals of Machine Learning
- Chapter 1 Introduction to Machine Learning
- What Is Machine Learning?
- Tools Commonly Used by Data Scientists
- Common Terminology
- Real-World Applications of Machine Learning
- Types of Machine Learning Systems
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised Learning
- Reinforcement Learning
- Batch Learning
- Incremental Learning
- Instance-based Learning
- Model-based Learning
- The Traditional Versus the Machine Learning Approach
- A Rule-based Decision System
- A Machine Learning-based System
- Summary
- Chapter 2 Data Collection and Preprocessing
- Machine Learning Datasets
- Scikit-learn Datasets
- AWS Public Datasets
- Kaggle.com Datasets
- UCI Machine Learning Repository
- Data Preprocessing Techniques
- Obtaining an Overview of the Data
- Handling Missing Values
- Creating New Features
- Transforming Numeric Features
- One-Hot Encoding Categorical Features
- Summary
- Chapter 3 Data Visualization with Python
- Introducing Matplotlib
- Components of a Plot
- Figure
- Axes
- Axis
- Axis Labels
- Grids
- Title
- Common Plots
- Histograms
- Bar Chart
- Grouped Bar Chart
- Stacked Bar Chart
- Stacked Percentage Bar Chart
- Pie Charts
- Box Plot
- Scatter Plots
- Summary
- Chapter 4 Creating Machine Learning Models with Scikit-learn
- Introducing Scikit-learn
- Creating a Training and Test Dataset
- K-Fold Cross Validation
- Creating Machine Learning Models
- Linear Regression
- Support Vector Machines
- Logistic Regression
- Decision Trees
- Summary
- Chapter 5 Evaluating Machine Learning Models
- Evaluating Regression Models
- RMSE Metric
- R2 Metric
- Evaluating Classification Models
- Binary Classification Models
- Multi-Class Classification Models
- Choosing Hyperparameter Values
- Summary
- Part 2 Machine Learning with Amazon Web Services
- Chapter 6 Introduction to Amazon Web Services
- What Is Cloud Computing?
- Cloud Service Models
- Cloud Deployment Models
- The AWS Ecosystem
- Machine Learning Application Services
- Machine Learning Platform Services
- Support Services
- Sign Up for an AWS Free-Tier Account
- Step 1: Contact Information
- Step 2: Payment Information
- Step 3: Identity Verification
- Step 4: Support Plan Selection
- Step 5: Confirmation
- Summary
- Chapter 7 AWS Global Infrastructure
- Regions and Availability Zones
- Edge Locations
- Accessing AWS
- The AWS Management Console
- Summary
- Chapter 8 Identity and Access Management
- Key Concepts
- Root Account
- User
- Identity Federation
- Group
- Policy
- Role
- Common Tasks
- Creating a User
- Modifying Permissions Associated with an Existing Group
- Creating a Role
- Securing the Root Account with MFA
- Setting Up an IAM Password Rotation Policy
- Summary
- Chapter 9 Amazon S3
- Key Concepts
- Bucket
- Object Key
- Object Value
- Version ID
- Storage Class
- Costs
- Subresources
- Object Metadata
- Common Tasks
- Creating a Bucket
- Uploading an Object
- Accessing an Object
- Changing the Storage Class of an Object
- Deleting an Object
- Amazon S3 Bucket Versioning
- Accessing Amazon S3 Using the AWS CLI
- Summary
- Chapter 10 Amazon Cognito
- Key Concepts
- Authentication
- Authorization
- Identity Provider
- Client
- OAuth 2.0
- OpenID Connect
- Amazon Cognito User Pool
- Identity Pool
- Amazon Cognito Federated Identities
- Common Tasks
- Creating a User Pool
- Retrieving the App Client Secret
- Creating an Identity Pool
- User Pools or Identity Pools: Which One Should You Use?
- Summary
- Chapter 11 Amazon DynamoDB
- Key Concepts
- Tables
- Global Tables
- Items
- Attributes
- Primary Keys
- Secondary Indexes
- Queries
- Scans
- Read Consistency
- Read/Write Capacity Modes
- Common Tasks
- Creating a Table
- Adding items to a Table
- Creating an Index
- Performing a Scan
- Performing a Query
- Summary
- Chapter 12 AWS Lambda
- Common Use Cases for Lambda
- Key Concepts
- Supported Languages
- Lambda Functions
- Programming Model
- Execution Environment
- Service Limitations
- Pricing and Availability
- Common Tasks
- Creating a Simple Python Lambda Function Using the AWS Management Console
- Testing a Lambda Function Using the AWS Management Console
- Deleting an AWS Lambda Function Using the AWS Management Console
- Summary
- Chapter 13 Amazon Comprehend
- Key Concepts
- Natural Language Processing
- Topic Modeling
- Language Support
- Pricing and Availability
- Text Analysis Using the Amazon Comprehend Management Console
- Interactive Text Analysis with the AWS CLI
- Entity Detection with the AWS CLI
- Key Phrase Detection with the AWS CLI
- Sentiment Analysis with the AWS CLI
- Using Amazon Comprehend with AWS Lambda
- Summary
- Chapter 14 Amazon Lex
- Key Concepts
- Bot
- Client Application
- Intent
- Slot
- Utterance
- Programming Model
- Pricing and Availability
- Creating an Amazon Lex Bot
- Creating Amazon DynamoDB Tables
- Creating AWS Lambda Functions
- Creating the Chatbot
- Customizing the AccountOverview Intent
- Customizing the ViewTransactionList Intent
- Testing the Chatbot
- Summary
- Chapter 15 Amazon Machine Learning
- Key Concepts
- Datasources
- ML Model
- Regularization
- Training Parameters
- Descriptive Statistics
- Pricing and Availability
- Creating Datasources
- Creating the Training Datasource
- Creating the Test Datasource
- Viewing Data Insights
- Creating an ML Model
- Making Batch Predictions
- Creating a Real-Time Prediction Endpoint for Your Machine Learning Model
- Making Predictions Using the AWS CLI
- Using Real-Time Prediction Endpoints with Your Applications
- Summary
- Chapter 16 Amazon SageMaker
- Key Concepts
- Programming Model
- Amazon SageMaker Notebook Instances
- Training Jobs
- Prediction Instances
- Prediction Endpoint and Endpoint Configuration
- Amazon SageMaker Batch Transform
- Data Channels
- Data Sources and Formats
- Built-in Algorithms
- Pricing and Availability
- Creating an Amazon SageMaker Notebook Instance
- Preparing Test and Training Data
- Training a Scikit-Learn Model on an Amazon SageMaker Notebook Instance
- Training a Scikit-Learn Model on a Dedicated Training Instance
- Training a Model Using a Built-in Algorithm on a Dedicated Training Instance
- Summary
- Chapter 17 Using Google TensorFlow with Amazon SageMaker
- Introduction to Google TensorFlow
- Creating a Linear Regression Model with Google TensorFlow
- Training and Deploying a DNN Classifier Using the TensorFlow Estimators API and Amazon SageMaker
- Summary
- Chapter 18 Amazon Rekognition
- Key Concepts
- Object Detection
- Object Location
- Scene Detection
- Activity Detection
- Facial Recognition
- Face Collection
- API Sets
- Non-Storage and Storage-Based Operations
- Model Versioning
- Pricing and Availability
- Analyzing Images Using the Amazon Rekognition Management Console
- Interactive Image Analysis with the AWS CLI
- Using Amazon Rekognition with AWS Lambda
- Creating the Amazon DynamoDB Table
- Creating the AWS Lambda Function
- Summary
- Appendix A Anaconda and Jupyter Notebook Setup
- Installing the Anaconda Distribution
- Creating a Conda Python Environment
- Installing Python Packages
- Installing Jupyter Notebook
- Summary
- Appendix B AWS Resources Needed to Use This Book
- Creating an IAM User for Development
- Creating S3 Buckets
- Appendix C Installing and Configuring the AWS CLI
- Mac OS Users
- Installing the AWS CLI
- Configuring the AWS CLI
- Windows Users
- Installing the AWS CLI
- Configuring the AWS CLI
- Appendix D Introduction to NumPy and Pandas
- NumPy
- Creating NumPy Arrays
- Modifying Arrays
- Indexing and Slicing
- Pandas
- Creating Series and Dataframes
- Getting Dataframe Information
- Selecting Data
- Index
- EULA
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