As the dataset grows, so do inconsistencies and errors. Whether as a result of human error, system failure, or data structure evolutions, real-world data is rife with invalid, absurd, or missing values. Even when the dataset is spotless, the nature of some variables need to be adapted to the model. We look at the most common data anomalies and characteristics that need to be corrected in the context of Amazon ML linear models.
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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