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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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Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Set original weight estimation at w_0 = 100g to initialize and a counter."

A block of code is set as follows:

# Create datasource for training
resource = name_id_generation('DS', 'training', trial)
print("Creating datasources for training (%s)"% resource['Name'] )

Any command-line input or output is written as follows:

$ aws s3 cp data/titanic.csv s3://aml.packt/data/ch9/

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Examples of reinforcement learning applications include AlphaGo, Google's world championship Go algorithm, self-driving cars, and semi-autonomous robots."

Warnings or important notes appear in a box like this.
Tips and tricks appear like this.

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