Book Image

Effective Amazon Machine Learning

By : Alexis Perrier
Book Image

Effective Amazon Machine Learning

By: Alexis Perrier

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 (17 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Dedication
Preface

Overview of a standard Amazon Machine Learning workflow


The Amazon Machine Learning service is available at https://console.aws.amazon.com/machinelearning/. The Amazon ML workflow closely follows a standard Data Science workflow with steps: 

  1. Extract the data and clean it up. Make it available to the algorithm.
  2. Split the data into a training and validation set, typically a 70/30 split with equal distribution of the predictors in each part.
  3. Select the best model by training several models on the training dataset and comparing their performances on the validation dataset.
  4. Use the best model for predictions on new data.

As shown in the following Amazon ML menu, the service is built around four objects:

  • Datasource
  • ML model
  • Evaluation
  • Prediction

The Datasource and Model can also be configured and set up in the same flow by creating a new Datasource and ML model. Let us take a closer look at each one of these steps.

The dataset

For the rest of the chapter, we will use the simple Predicting Weight by Height...