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Applied Supervised Learning with R : Use Machine Learning Libraries of R to Build Models That Solve Business Problems and Predict Future Trends.
Applied Supervised Learning with R will make you a pro at identifying your business problem, selecting the best supervised machine learning algorithm to solve it, and fine-tuning your model to exactly deliver your needs without overfitting itself.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2019.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Ramasubramanian, Karthik. | |
| 245 | 1 | 0 | |a Applied Supervised Learning with R : |b Use Machine Learning Libraries of R to Build Models That Solve Business Problems and Predict Future Trends. |
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2019. | ||
| 300 | |a 1 online resource (503 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; FM; Table of Contents; Preface; Chapter 1: R for Advanced Analytics; Introduction; Working with Real-World Datasets; Exercise 1: Using the unzip Method for Unzipping a Downloaded File; Reading Data from Various Data Formats; CSV Files; Exercise 2: Reading a CSV File and Summarizing its Column; JSON; Exercise 3: Reading a JSON file and Storing the Data in DataFrame; Text; Exercise 4: Reading a CSV File with Text Column and Storing the Data in VCorpus; Write R Markdown Files for Code Reproducibility; Activity 1: Create an R Markdown File to Read a CSV File and Write a Summary of Data | |
| 505 | 8 | |a Data Structures in RVector; Matrix; Exercise 5: Performing Transformation on the Data to Make it Available for the Analysis; List; Exercise 6: Using the List Method for Storing Integers and Characters Together; Activity 2: Create a List of Two Matrices and Access the Values; DataFrame; Exercise 7: Performing Integrity Checks Using DataFrame; Data Table; Exercise 8: Exploring the File Read Operation; Data Processing and Transformation; cbind; Exercise 9: Exploring the cbind Function; rbind; Exercise 10: Exploring the rbind Function; The merge Function; Exercise 11: Exploring the merge Function | |
| 505 | 8 | |a Inner JoinLeft Join; Right Join; Full Join; The reshape Function; Exercise 12: Exploring the reshape Function; The aggregate Function; The Apply Family of Functions; The apply Function; Exercise 13: Implementing the apply Function; The lapply Function; Exercise 14: Implementing the lapply Function; The sapply Function; The tapply Function; Useful Packages; The dplyr Package; Exercise 15: Implementing the dplyr Package; The tidyr Package; Exercise 16: Implementing the tidyr Package | |
| 505 | 8 | |a Activity 3: Create a DataFrame with Five Summary Statistics for All Numeric Variables from Bank Data Using dplyr and tidyrThe plyr Package; Exercise 17: Exploring the plyr Package; The caret Package; Data Visualization; Scatterplot; Scatter Plot between Age and Balance split by Marital Status; Line Charts; Histogram; Boxplot; Summary; Chapter 2: Exploratory Analysis of Data; Introduction; Defining the Problem Statement; Problem-Designing Artifacts; Understanding the Science Behind EDA; Exploratory Data Analysis; Exercise 18: Studying the Data Dimensions; Univariate Analysis | |
| 505 | 8 | |a Exploring Numeric/Continuous FeaturesExercise 19: Visualizing Data Using a Box Plot; Exercise 20: Visualizing Data Using a Histogram; Exercise 21: Visualizing Data Using a Density Plot; Exercise 22: Visualizing Multiple Variables Using a Histogram; Activity 4: Plotting Multiple Density Plots and Boxplots; Exercise 23: Plotting a Histogram for the nr.employed, euribor3m, cons.conf.idx, and duration Variables; Exploring Categorical Features; Exercise 24: Exploring Categorical Features; Exercise 25: Exploring Categorical Features Using a Bar Chart | |
| 500 | |a Exercise 26: Exploring Categorical Features using Pie Chart | ||
| 520 | |a Applied Supervised Learning with R will make you a pro at identifying your business problem, selecting the best supervised machine learning algorithm to solve it, and fine-tuning your model to exactly deliver your needs without overfitting itself. | ||
| 504 | |a Includes bibliographical references. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Data capture & analysis. |2 bicssc | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 700 | 1 | |a Moolayil, Jojo. | |
| 758 | |i has work: |a Applied supervised learning with R (Text) |1 https://id.oclc.org/worldcat/entity/E39PCYyVvMXVjM8qfTqFgGjw83 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Ramasubramanian, Karthik. |t Applied Supervised Learning with R : Use Machine Learning Libraries of R to Build Models That Solve Business Problems and Predict Future Trends. |d Birmingham : Packt Publishing, Limited, ©2019 |z 9781838556334 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5784240 |z Texto completo |
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| 938 | |a YBP Library Services |b YANK |n 300576902 | ||
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