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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

Summary

In this chapter, we covered different types of statistical inferences for hypothesis testing, targeting both numerical and categorical data. We introduced inference methods for a single variable, two variables, and multiple variables, using either proportion (for categorical variable) or mean (for numerical variable) as the sample statistic. The hypothesis testing procedure, including both the parametric approach using model-based approximation and the non-parametric approach using bootstrap-based simulations, offers valuable tools such as the confidence interval and p-value. These tools allow us to make a decision about whether we can reject the null hypothesis in favor of the alternative hypothesis. Such a decision also relates to the Type I and Type II errors.

In the next chapter, we will cover one of the most widely used statistical and ML models: linear regression.

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The Statistics and Machine Learning with R Workshop
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