Data Science, Analytics and Machine Learning with R
eBook - ePub

Data Science, Analytics and Machine Learning with R

  1. 660 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Data Science, Analytics and Machine Learning with R

About this book

Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning. In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear. - Presents a comprehensive and practical overview of machine learning, data mining and AI techniques for a broad multidisciplinary audience - Serves readers who are interested in statistics, analytics and modeling, and those who wish to deepen their knowledge in programming through the use of R - Teaches readers how to apply machine learning techniques to a wide range of data and subject areas - Presents data in a graphically appealing way, promoting greater information transparency and interactive learning

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Yes, you can access Data Science, Analytics and Machine Learning with R by Luiz Paulo Favero,Patricia Belfiore,Rafael de Freitas Souza in PDF and/or ePUB format, as well as other popular books in Informatik & Künstliche Intelligenz (KI) & Semantik. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Front Matter
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. Epigraph
  7. List of Illustrations
  8. List of Tables
  9. Chapter 1 : Overview of data science, analytics, and machine learning
  10. Chapter 2 : Introduction to R-based language
  11. Chapter 3 : Types of variables, measurement scales, and accuracy scales*
  12. Chapter 4 : Univariate descriptive statistics
  13. Chapter 5 : Bivariate descriptive statistics
  14. Chapter 6 : Hypotheses tests
  15. Chapter 7 : Data visualization and multivariate graphs
  16. Chapter 8 : Webscraping and handcrafted robots
  17. Chapter 9 : Using application programming interfaces to collect data
  18. Chapter 10 : Managing data
  19. Chapter 11 : Cluster analysis
  20. Chapter 12 : Principal component factor analysis
  21. Chapter 13 : Simple and multiple correspondence analysis
  22. Chapter 14 : Simple and multiple regression models
  23. Chapter 15 : Binary and multinomial logistic regression models
  24. Chapter 16 : Count-data and zero-inflated regression models
  25. Chapter 17 : Generalized linear mixed models
  26. Chapter 18 : Support vector machines
  27. Chapter 19 : Classification and regression trees
  28. Chapter 20 : Boosting and bagging
  29. Chapter 21 : Random forests
  30. Chapter 22 : Artificial neural networks
  31. Chapter 23 : Working on shapefiles
  32. Chapter 24 : Dealing with simple feature objects
  33. Chapter 25 : Raster objects
  34. Chapter 26 : Exploratory spatial analysis
  35. Chapter 27 : Enhanced and interactive graphs
  36. Chapter 28 : Dashboards with R
  37. Answers
  38. GENERIC-TITLE
  39. Index
  40. A