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Effective Amazon Machine Learning
Effective Amazon Machine Learning

Effective Amazon Machine Learning: Expert web services for machine learning on cloud

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Effective Amazon Machine Learning

Machine Learning Definitions and Concepts

This chapter offers a high-level definition and explanation of the machine learning concepts needed to use the Amazon Machine Learning (Amazon ML) service and fully understand how it works. The chapter has three specific goals:

  • Listing the main techniques to improve the quality of predictions used when dealing with raw data. You will learn how to deal with the most common types of data problems. Some of these techniques are available in Amazon ML, while others aren't.
  • Presenting the predictive analytics workflow and introducing the concept of cross validation or how to split your data to train and test your models.
  • Showing how to detect poor performance of your model and presenting strategies to improve these performances.

The reader will learn the following:

  • How to spot common problems and anomalies within a given dataset
  • How to extract the most information out...

What's an algorithm? What's a model?

Before we dive into data munging, let's take a moment to explain the difference between an algorithm and a model, two terms we've been using up until now without a formal definition.

Consider the simple linear regression example we saw in Chapter 1, Introduction to Machine Learning and Predictive Analytics — the linear regression equation with one predictor:

Here, x is the variable, ŷ the prediction, not the real value, and (a,b) the parameters of the linear regression model:

  • The conceptual or theoretical model is the representation of the data that is the most adapted to the actual dataset. It is chosen at the beginning by the data scientist. In this case, the conceptual model is the linear regression model, where the prediction is a linear combination of a variable. Other conceptual models include decision trees, naive bayes, neural networks...

Dealing with messy data

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.

Classic datasets versus real-world datasets

Data scientists and machine-learning practitioners often use classic datasets to demonstrate the behavior of certain models. The Iris dataset, composed of 150 samples of three types of iris flowers, is one of the most commonly used to demonstrate or to teach...

The predictive analytics workflow

We have been talking about training the model. What does that mean in practice?

In supervised learning, the dataset is usually split into three non-equal parts: training, validation, and test:

  • The training set on which you train your model. It has to be big enough to give the model as much information on the data as possible. This subset of the data is used by the algorithm to estimate the best parameters of the model. In our case, the SGD algorithm will use that training subset to find the optimal weights of the linear regression model.
  • The validation set is used to assess the performance of a trained model. By measuring the performance of the trained model on a subset that has not been used in its training, we have an objective assessment of its performance. That way we can train different models with different meta parameters and see which one is performing the...

Identifying and correcting poor performances

A performant predictive model is one that produces reliable and satisfying predictions on new data. There are two situations where the model will fail to consistently produce good predictions, and both depend on how the model is trained. A poorly trained model will result in underfitting, while an overly trained model will result in overfitting.

Underfitting

Underfitting means that the model was poorly trained. Either the training dataset did not have enough information to infer strong predictions, or the algorithm that trained the model on the training dataset was not adequate for the context. The algorithm was not well parameterized or simply inadequate for the data.

If we measure the prediction error...

What's an algorithm? What's a model?


Before we dive into data munging, let's take a moment to explain the difference between an algorithm and a model, two terms we've been using up until now without a formal definition.

Consider the simple linear regression example we saw in Chapter 1, Introduction to Machine Learning and Predictive Analytics — the linear regression equation with one predictor:

Here, x is the variable, ŷ the prediction, not the real value, and (a,b) the parameters of the linear regression model:

  • The conceptual or theoretical model is the representation of the data that is the most adapted to the actual dataset. It is chosen at the beginning by the data scientist. In this case, the conceptual model is the linear regression model, where the prediction is a linear combination of a variable. Other conceptual models include decision trees, naive bayes, neural networks, and so on. All these models have parameters that need to be tuned to the actual data.
  • The algorithm is the computational...

Dealing with messy data


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.

Classic datasets versus real-world datasets

Data scientists and machine-learning practitioners often use classic datasets to demonstrate the behavior of certain models. The Iris dataset, composed of 150 samples of three types of iris flowers, is one of the most commonly used to demonstrate or to teach predictive analytics. It has been around since 1936!

The Boston housing dataset and the Titanic dataset are other very popular datasets for predictive analytics. For text classification, the Reuters or the 20 newsgroups text datasets are very common...

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Key benefits

  • 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

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.

Who is this book for?

This book is intended for data scientists and managers of predictive analytics projects; it will teach beginner- to advanced-level machine learning practitioners how to leverage Amazon Machine Learning and complement their existing Data Science toolbox. No substantive prior knowledge of Machine Learning, Data Science, statistics, or coding is required.

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

Product Details

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Publication date : Apr 25, 2017
Length: 306 pages
Edition : 1st
Language : English
ISBN-13 : 9781785883231
Vendor :
Amazon
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Product Details

Publication date : Apr 25, 2017
Length: 306 pages
Edition : 1st
Language : English
ISBN-13 : 9781785883231
Vendor :
Amazon
Category :
Languages :
Tools :

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Table of Contents

9 Chapters
Introduction to Machine Learning and Predictive Analytics Chevron down icon Chevron up icon
Machine Learning Definitions and Concepts Chevron down icon Chevron up icon
Overview of an Amazon Machine Learning Workflow Chevron down icon Chevron up icon
Loading and Preparing the Dataset Chevron down icon Chevron up icon
Model Creation Chevron down icon Chevron up icon
Predictions and Performances Chevron down icon Chevron up icon
Command Line and SDK Chevron down icon Chevron up icon
Creating Datasources from Redshift Chevron down icon Chevron up icon
Building a Streaming Data Analysis Pipeline Chevron down icon Chevron up icon
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