Learn the fundamentals of statistics and machine learning using R libraries for data processing, visualization, model training, and statistical inference
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
Getting Started with R
Data Processing with dplyr
Intermediate Data Processing
Data Visualization with ggDescription2
Exploratory Data Analysis
Effective Reporting with R Markdown
Linear Algebra in R
Intermediate Linear Algebra in R
Calculus in R
Probability Basics
Statistics Estimation
Linear Regression in R
Logistic Regression in R
Bayesian Statistics