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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 common probability distributions. We started by introducing discrete probability distributions, including the Bernoulli distribution, the binomial distribution, the Poisson distribution, and the geometric distribution. We followed by covering common continuous probability distributions, including the normal distribution, the exponential distribution, and the uniform distribution. Next, we introduced common sampling distributions and their use in statistical inferences for population statistics. Finally, we covered order statistics and their use in calculating the VaR in the context of daily stock returns.

In the next chapter, we will cover statistical estimation procedures, including point estimation, the central limit theorem, and the confidence interval.

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The Statistics and Machine Learning with R Workshop
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