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Introduction to Machine Learning with R: Rigorous Mathematical Analysis

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Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you’ll first start to learn with regression modelling and then move into more advanced topics such as neural networks and tree-based methods.

Finally, you’ll delve into the frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression and classification models, you’ll be able to solve an array of machine learning problems. Author Scott V. Burger provides several examples to help you build a working knowledge of machine learning.

  • Explore machine learning models, algorithms, and data training
  • Understand machine learning algorithms for supervised and unsupervised cases
  • Examine statistical concepts for designing data for use in models
  • Dive into linear regression models used in business and science
  • Use single-layer and multilayer neural networks for calculating outcomes
  • Look at how tree-based models work, including popular decision trees
  • Get a comprehensive view of the machine learning ecosystem in R
  • Explore the powerhouse of tools available in R’s caret package
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Applied Graph Theory in Computer Vision and Pattern Recognition (Studies in Computational Intelligence)
Applied Graph Theory in Computer Vision and Pattern Recognition (Studies in Computational Intelligence)
This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to...
Deep Learning Cookbook: Practical Recipes to Get Started Quickly
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MATLAB Recipes: A Problem-Solution Approach
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Advances in Biomedical Engineering
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R Machine Learning By Example
R Machine Learning By Example

Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully

About This Book

  • Get to grips with the concepts of machine learning through exciting real-world examples
  • Visualize and solve complex problems...
UNDERSTANDING STATISTICS
UNDERSTANDING STATISTICS
This is a book on the understanding of statistical concepts. If you have no knowledge, you will receive basic knowledge, without having to worry much about mathematics. And if you already know something about statistical methods, you will get a better understanding of the ideas behind them. All basic concepts are discussed in detail and...
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