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by Abhishek Mishra
Machine Learning in the AWS Cloud
Cover
Acknowledgments
About the Author
About the Technical Editor
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?
Types of Machine Learning Systems
The Traditional Versus the Machine Learning Approach
Summary
Chapter 2: Data Collection and Preprocessing
Machine Learning Datasets
Data Preprocessing Techniques
Summary
Chapter 3: Data Visualization with Python
Introducing Matplotlib
Components of a Plot
Common Plots
Summary
Chapter 4: Creating Machine Learning Models with Scikit-learn
Introducing Scikit-learn
Creating a Training and Test Dataset
Creating Machine Learning Models
Summary
Chapter 5: Evaluating Machine Learning Models
Evaluating Regression Models
Evaluating 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
Sign Up for an AWS Free-Tier Account
Summary
Note
Chapter 7: AWS Global Infrastructure
Regions and Availability Zones
Edge Locations
Accessing AWS
Summary
Chapter 8: Identity and Access Management
Key Concepts
Common Tasks
Summary
Chapter 9: Amazon S3
Key Concepts
Common Tasks
Summary
Chapter 10: Amazon Cognito
Key Concepts
Common Tasks
User Pools or Identity Pools: Which One Should You Use?
Summary
Chapter 11: Amazon DynamoDB
Key Concepts
Common Tasks
Summary
Chapter 12: AWS Lambda
Common Use Cases for Lambda
Key Concepts
Common Tasks
Summary
Chapter 13: Amazon Comprehend
Key Concepts
Text Analysis Using the Amazon Comprehend Management Console
Interactive Text Analysis with the AWS CLI
Using Amazon Comprehend with AWS Lambda
Summary
Chapter 14: Amazon Lex
Key Concepts
Creating an Amazon Lex Bot
Summary
Chapter 15: Amazon Machine Learning
Key Concepts
Creating Datasources
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
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
Analyzing Images Using the Amazon Rekognition Management Console
Interactive Image Analysis with the AWS CLI
Using Amazon Rekognition with AWS Lambda
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
Windows Users
Appendix D: Introduction to NumPy and Pandas
NumPy
Pandas
Index
End User License Agreement
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