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Effective Amazon Machine Learning

Effective Amazon Machine Learning

By : Perrier
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Effective Amazon Machine Learning

Effective Amazon Machine Learning

By: 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 (10 chapters)
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Introducing polynomial regression


In two dimensions, where we have a predictor and an outcome, linear modeling is all about finding the best line that approximates your data. In three dimensions (two predictors and one outcome), the idea is then to find the best plane, or the best flat surface, that approximates your data. In the N dimension, the surface becomes an hyperplane, but the goal is always the same – to find the hyperplane of dimension N-1 that gives the best approximation for regression or that separates the classes the best for classification. That hyperplane is always flat.

Coming back to the very non-linear two-dimensional dataset we created, it is obvious that no line can properly approximate the relation between the predictor and the outcome. There are many different methods to model non-linear data, including polynomial regression, step functions, splines, and Generalized additive models (GAM). See Chapter 7 of An Introduction to Statistical Learning by James, Witten, Hastie...

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