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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

Linear Regression in R

In this chapter, we will introduce linear regression, a fundamental statistical approach that’s used to model the relationship between a target variable and multiple explanatory (also called independent) variables. We will cover the basics of linear regression, starting with simple linear regression and then extending the concepts to multiple linear regression. We will learn how to estimate the model coefficients, evaluate the goodness of fit, and test the significance of the coefficients using hypothesis testing. Additionally, we will discuss the assumptions underlying linear regression and explore techniques to address potential issues, such as nonlinearity, interaction effect, multicollinearity, and heteroskedasticity. We will also introduce two widely used regularization techniques: the ridge and Least Absolute Shrinkage and Selection Operator (lasso) penalties.

By the end of this chapter, you will learn the core principles of linear regression...

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The Statistics and Machine Learning with R Workshop
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