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#Practical Machine Learning Cookbook This is the code repository for Practical Machine Learning Cookbook, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

Data in today’s world is the new black gold which is growing exponentially. This growth can be attributed to the growth of existing data, and new data in a structured and unstructured format from multiple sources such as social media, Internet, documents and the Internet of Things. The flow of data must be collected, processed, analyzed, and finally presented in real time to ensure that the consumers of the data are able to take informed decisions in today’s fast-changing environment. Machine learning techniques are applied to the data using the context of the problem to be solved to ensure that fast arriving and complex data can be analyzed in a scientific manner using statistical techniques. Using machine learning algorithms that iteratively learn from data, hidden patterns can be discovered. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt and learn to produce reliable decisions from new data sets. ##Instructions and Navigation All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

Chapter 1 covers overviews of concepts and does not contain code. The required support files can be found in the Data folder within the chapter folders.

The code will look like the following:

install.packages("ggplot2")

This book is focused on building machine learning-based applications in R. We have used R to build various solutions. We focused on how to utilize various R libraries and functions in the best possible way to overcome real-world challenges. We have tried to keep all the code as friendly and readable as possible. We feel that this will enable our readers to easily understand the code and readily use it in different scenarios.

• Python Machine Learning

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• Practical Machine Learning

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