The Statistics and Machine Learning with R Workshop: The essential guide on efficient and effective modeling for data scientists

Key Features

• Advance your ML career with the help of detailed explanations, intuitive illustrations, and code examples
• Gain practical insights into the real-world applications of statistics and machine learning
• Explore the technicalities of statistics and machine learning for effective data presentation
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

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.

What you will learn

• 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

Who this book is for

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.

Table of Contents

1. Getting Started with R
2. Data Processing with dplyr
3. Intermediate Data Processing
4. Data Visualization with ggplot2
5. Exploratory Data Analysis
6. Effective Reporting with R Markdown
7. Linear Algebra in R
8. Intermediate Linear Algebra in R
9. Calculus in R
10. Probability Basics
11. Statistics Estimation
12. Linear Regression in R
13. Logistic Regression in R
14. Bayesian Statistics

Peng Liu 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 ten years of working experience as a data scientist across the banking, technology, and hospitality industries.