Signing up for an AWS account is straightforward. Go to http://aws.amazon.com/, and choose Create a AWS Account. If you don't have an AWS account yet, take advantage of the free tier access. New free-tier accounts enjoy free resources up to a certain limit for up to 12 months. These free resources are available for many AWS services, such as EC2, S3, RDS or Redshift, and so forth. Unfortunately, Amazon Machine Learning is not included in the AWS Free Tier. You will be billed for your Amazon ML usage. However, since Amazon ML requires your data to be stored on S3 or another AWS source such as RedShift, which are included in the Free Tier offer, it will still be advantageous to start with a free tier account. Follow the instructions to open a free tier account. You will be asked for your name, e-mail, address, phone number, and payment information.
Effective Amazon Machine Learning
By :
Effective Amazon Machine Learning
By:
Overview of this book
Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. Following AWS simplifying Machine learning, this book will help you bring predictive analytics projects to fruition in three easy steps: data preparation, model tuning, and model selection.
This book will introduce you to the Amazon Machine Learning platform and will implement core data science concepts such as classification, regression, regularization, overfitting, model selection, and evaluation. Furthermore, you will learn to leverage the Amazon Web Service (AWS) ecosystem for extended access to data sources, implement realtime predictions, and run Amazon Machine Learning projects via the command line and the Python SDK.
Towards the end of the book, you will also learn how to apply these services to other problems, such as text mining, and to more complex datasets.
Table of Contents (10 chapters)
Preface
Free Chapter
Introduction to Machine Learning and Predictive Analytics
Machine Learning Definitions and Concepts
Overview of an Amazon Machine Learning Workflow
Loading and Preparing the Dataset
Model Creation
Predictions and Performances
Command Line and SDK
Creating Datasources from Redshift
Building a Streaming Data Analysis Pipeline
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