Effective Amazon Machine Learning

More Information
  • 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 
  • Leverage the Amazon Web Service ecosystem to access extended data sources
  • Implement streaming and advanced projects

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.

  • 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
Page Count 306
Course Length 9 hours 10 minutes
ISBN 9781785883231
Date Of Publication 24 Apr 2017


Alexis Perrier

Alexis Perrier is a data science consultant with experience in signal processing and stochastic algorithms. He holds a master's in mathematics from Université Pierre et Marie Curie Paris VI and a PhD in signal processing from Télécom ParisTech. He is actively involved in the DC data science community. He is also an avid book lover and proud owner of a real chalk blackboard, where he regularly shares his fascination of mathematical equations with his kids.