This is the code repository for The Statistics and Machine Learning with R Workshop, published by Packt.
Unlock the power of efficient data science modeling with this hands-on guide
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.
This book covers the following exciting features:
- Hone your skills in different probability distributions and hypothesis testing
- Explore the fundamentals of linear algebra and calculus
- Master crucial statistics and machine learning concepts in theory and practice
- Discover essential data processing and visualization techniques
- Engage in interactive data analysis using R
- Use R to perform statistical modeling, including Bayesian and linear regression
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
lm_model = lm(Class_num ~ Duration, data=GermanCredit)
coefs = coefficients(lm_model)
intercept = coefs[1]
slope = coefs[2]
Following is what you need for this book: This book is for beginner to intermediate-level data scientists, undergraduate to masters-level students, and early to mid-senior data scientists or analysts looking to expand their knowledge of machine learning by exploring various R libraries. Basic knowledge of linear algebra and data modeling is a must.
With the following software and hardware list you can run all code files present in the book (Chapter 1-14).
To get the most out of this book, it’s advisable to have a basic understanding of programming, ideally in R, although a strong foundation in any programming language should suffice. Familiarity with elementary statistics and mathematical concepts will also be beneficial, as the book delves into statistical methods and mathematical models. While the book is structured to guide you from foundational to advanced topics, prior exposure to data analysis techniques will enhance your learning experience. If you’re new to R, you may want to spend some extra time on the initial chapters to become comfortable with the programming environment and syntax.
System requirements are mentioned in the following table:
| Software/Hardware | Operating System requirements |
|---|---|
| R | Windows, Mac OS X, and Linux (Any) |
Liu Peng is an assistant professor of quantitative finance (practice) at Singapore Management University and an adjunct researcher at the National University of Singapore. He holds a Ph.D. in statistics from the National University of Singapore and has 10 years of working experience as a data scientist across the banking, technology, and hospitality industries.