Effective Amazon Machine Learning

Alexis Perrier

ISBN 10: 1785883232 / ISBN 13: 9781785883231

Published by Packt Publishing 2017-04-25, 2017

About this Item

Seller Inventory # 6666-IUK-9781785883231

Bibliographic Details

• Title: Effective Amazon Machine Learning
• Publisher: Packt Publishing 2017-04-25
• Publication Date: 2017
• Language: English
• ISBN 10: 1785883232
• ISBN 13: 9781785883231
• Binding: Paperback
• Condition: New

About this title

Synopsis

Key Features

• Create great machine learning models that combine the power of algorithms with interactive tools without worrying about the underlying complexity
• Learn the What's next? of machine learning―machine learning on the cloud―with this unique guide
• Create web services that allow you to perform affordable and fast machine learning on the cloud

Book Description

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.

What you will learn

• Learn how to use the Amazon Machine Learning service from scratch for predictive analytics
• Gain hands-on experience of key Data Science concepts
• Solve classic regression and classification problems
• Run projects programmatically via the command line and the Python SDK

About the Author

Alexis Perrier

Alexis Perrier is a data scientist at Docent Health, a Boston-based startup. He works with Machine Learning and Natural Language Processing to improve patient experience in healthcare. Fascinated by the power of stochastic algorithms, he is actively involved in the data science community as an instructor, blogger, and presenter. He holds a Ph.D. in Signal Processing from Telecom ParisTech and resides in Boston, MA. You can get in touch with him on twitter @alexip and by email at alexis.perrier@gmail.com.