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The Statistics and Machine Learning with R Workshop

The Statistics and Machine Learning with R Workshop

By : Liu Peng
4.6 (5)
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The Statistics and Machine Learning with R Workshop

The Statistics and Machine Learning with R Workshop

4.6 (5)
By: Liu Peng

Overview of this book

The Statistics and Machine Learning with R Workshop is a comprehensive resource packed with insights into statistics and machine learning, along with a deep dive into R libraries. The learning experience is further enhanced by practical examples and hands-on exercises that provide explanations of key concepts. Starting with the fundamentals, you’ll explore the complete model development process, covering everything from data pre-processing to model development. In addition to machine learning, you’ll also delve into R's statistical capabilities, learning to manipulate various data types and tackle complex mathematical challenges from algebra and calculus to probability and Bayesian statistics. You’ll discover linear regression techniques and more advanced statistical methodologies to hone your skills and advance your career. By the end of this book, you'll have a robust foundational understanding of statistics and machine learning. You’ll also be proficient in using R's extensive libraries for tasks such as data processing and model training and be well-equipped to leverage the full potential of R in your future projects.
Table of Contents (20 chapters)
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1
Part 1:Statistics Essentials
8
Part 2:Fundamentals of Linear Algebra and Calculus in R
12
Part 3:Fundamentals of Mathematical Statistics in R

Intermediate Linear Algebra in R

The previous chapter covered the basics of linear algebra and its calculations in R. This chapter will go a step further by extending to intermediate linear algebra and cover topics such as the determinant, rank, and trace of a matrix, eigenvalues and eigenvectors, and principal component analysis (PCA). Besides providing an intuitive understanding of these abstract yet important mathematical concepts, we’ll cover the practical implementations of calculating these quantities in R.

By the end of this chapter, you will have grasped important matrix properties, such as determinant and rank, and gained hands-on experience in calculating these quantities.

In this chapter, we will cover the following topics:

  • Introducing the matrix determinant
  • Introducing the matrix trace
  • Understanding the matrix norm
  • Getting to know eigenvalues and eigenvectors
  • Introducing principal component analysis
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Tech Concepts
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Programming languages
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The Statistics and Machine Learning with R Workshop
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