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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 essential functions and techniques for data transformation, aggregation, and merging. For data transformation at the row level, we learned about common utility functions such as filter(), mutate(), select(), arrange(), top_n(), and transmute(). For data aggregation, which summarizes the raw dataset into a smaller and more concise summary view, we introduced functions such as count(), group_by(), and summarize(). For data merging, which combines multiple datasets into one, we learned about different joining methods, including inner_join(), left_join(), right_join(), and full_join(). Although there are other more advanced joining functions, the essential tools we covered in our toolkit are enough for us to achieve the same task. Finally, we went through a case study based on the Stack Overflow dataset. The skills we learned in this chapter will come in very handy in many data analysis tasks.

In the next chapter, we will cover a more advanced topic...

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The Statistics and Machine Learning with R Workshop
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