The Statistics and Machine Learning with R Workshop : Unlock the power of efficient data science modeling with this hands-on guide
Language: English
Published by Packt Publishing, 2023
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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nach der Bestellung gedruckt Neuware - Printed after ordering - Learn the fundamentals of statistics and machine learning using R libraries for data processing, visualization, model training, and statistical inferenceKey Features:Advance your ML career with the help of detailed explanations, intuitive illustrations, and code examplesGain practical insights into the real-world applications of statistics and machine learningExplore the technicalities of statistics and machine learning for effective data presentationPurchase of the print or Kindle book includes a free PDF Elektronisches BuchBook 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 testingExplore the fundamentals of linear algebra and calculusMaster crucial statistics and machine learning concepts in theory and practiceDiscover essential data processing and visualization techniquesEngage in interactive data analysis using RUse R to perform statistical modeling, including Bayesian and linear regressionWho 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.…
Seller Inventory # 9781803240305
- Title
- The Statistics and Machine Learning with R Workshop : Unlock the power of efficient data science modeling with this hands-on guide
- Author
- Liu Peng
- Publisher
- Packt Publishing
- Publication year
- 2023
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 180324030X
- ISBN 13
- 9781803240305
- Item weight
- 953 grams
- Dimensions
- 235x191x28 mm
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 ggplot2
- 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
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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