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R Machine Learning Projects

R Machine Learning Projects

By : Dr. Sunil Kumar Chinnamgari
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R Machine Learning Projects

R Machine Learning Projects

1 (1)
By: Dr. Sunil Kumar Chinnamgari

Overview of this book

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.
Table of Contents (19 chapters)
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Part 1: Object-Oriented Design Principles
6
Part 2: Basic Design Patterns
12
Part 3: Advanced Design Patterns

Summary

In this chapter, we learned that changing the behavior of objects using the State pattern allows for more manageable and scalable code. The State pattern is a behavioral design pattern that helps an object alter its behavior when its internal state changes. This pattern provides a cleaner and more organized way to handle state transitions compared to using numerous conditional statements. Each state is represented as a separate class, and the context object delegates state-specific behavior to these classes. By encapsulating state-related behavior, we achieve better code maintainability.

On top of that, the chapter illustrated how state transitions can be implemented in Godot Engine. We explored how to use the AnimationTree node to handle animations and transitions based on the object’s state. Advance expressions were introduced as a way to create more complex transition conditions. These expressions are logical statements that return true or false, allowing for dynamic...

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R Machine Learning Projects
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