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Effective Amazon Machine Learning

 

Effective Amazon Machine Learning

Autor: Alexis Perrier
Broj strana: 306
ISBN broj: 9781785883231
Izdavač: PACKT PUBLISHING
Godina izdanja: 2017.

Pregleda (30 dana / ukupno): 28 / 258

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

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.

Authors

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.

Table of Contents

Chapter 1: Introduction to Machine Learning and Predictive Analytics
Chapter 2: Machine Learning Definitions and Concepts
Chapter 3: Overview of an Amazon Machine Learning Workflow
Chapter 4: Loading and Preparing the Dataset
Chapter 5: Model Creation
Chapter 6: Predictions and Performances
Chapter 7: Command Line and SDK
Chapter 8: Creating Datasources from Redshift
Chapter 9: Building a Streaming Data Analysis Pipeline

 

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