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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

This chapter provides a comprehensive introduction to Bayesian statistics, beginning with an exploration of the fundamental Bayes’ theorem. We delved into its components, starting with understanding the generative model, which helps us simulate data and examine how changes in parameters affect the data generation process.

We then focused on understanding the prior distribution, an essential part of Bayesian statistics that represents our prior knowledge about an uncertain parameter. This was followed by an introduction to the likelihood function, a statistical function that determines how likely it is for a set of observations to occur given specific parameter values.

Next, we introduced the concept of the posterior model. This combines our prior distribution and likelihood to give a new probability distribution that represents updated beliefs after having seen the data. We also explored more complex models, such as the normal-normal model, wherein both the likelihood...

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The Statistics and Machine Learning with R Workshop
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