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Machine Learning Using R.

Author: Karthik Ramasubramanian; Abhishek Singh
Publisher: Berkeley, CA : Apress, 2016.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Ramasubramanian, Karthik.
Machine Learning Using R.
Berkeley, CA : Apress, ©2016
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Karthik Ramasubramanian; Abhishek Singh
ISBN: 9781484223345 1484223349
OCLC Number: 967530052
Notes: 3.2.11 Sampling with Replacement (SWR).
Description: 1 online resource (580 pages)
Contents: At a Glance; Contents; About the Authors; About the Technical Reviewer; Acknowledgments; Chapter 1: Introduction to Machine Learning and R; 1.1 Understanding the Evolution; 1.1.1 Statistical Learning; 1.1.2 Machine Learning (ML); 1.1.3 Artificial Intelligence (AI); 1.1.4 Data Mining; 1.1.5 Data Science; 1.2 Probability and Statistics; 1.2.1 Counting and Probability Definition; 1.2.2 Events and Relationships; 1.2.2.1 Independent Events; 1.2.2.2 Conditional Independence; 1.2.2.3 Bayes Theorem; 1.2.3 Randomness, Probability, and Distributions. 1.2.4 Confidence Interval and Hypothesis Testing1.2.4.1 Confidence Interval; 1.2.4.2 Hypothesis Testing; 1.3 Getting Started with R; 1.3.1 Basic Building Blocks; 1.3.1.1 Calculations; 1.3.1.2 Statistics with R; 1.3.1.3 Packages; 1.3.2 Data Structures in R; 1.3.2.1 Vectors; 1.3.2.2 List; 1.3.2.3 Matrix; 1.3.2.4 Data Frame; 1.3.3 Subsetting; 1.3.3.1 Vectors; 1.3.3.2 Lists; 1.3.3.3 Matrixes; 1.3.3.4 Data Frames; 1.3.4 Functions and Apply Family; 1.4 Machine Learning Process Flow; 1.4.1 Plan; 1.4.2 Explore; 1.4.3 Build; 1.4.4 Evaluate; 1.5 Other Technologies; 1.6 Summary; 1.7 References. Chapter 2: Data Preparation and Exploration2.1 Planning the Gathering of Data; 2.1.1 Variables Types; 2.1.1.1 Categorical Variables; 2.1.1.2 Continuous Variables; 2.1.2 Data Formats; 2.1.2.1 Comma-Separated Values; 2.1.2.2 Microsoft Excel; 2.1.2.3 Extensible Markup Language: XML; 2.1.2.4 Hypertext Markup Language: HTML; 2.1.2.5 JSON; 2.1.2.6 Other Formats; 2.1.3 Data Sources; 2.1.3.1 Structured; 2.1.3.2 Semi-Structured; 2.1.3.3 Unstructured; 2.2 Initial Data Analysis (IDA); 2.2.1 Discerning a First Look; 2.2.1.1 Function str(); 2.2.1.2 Naming Convention: make.names(). 2.2.1.3 Table(): Pattern or Trend2.2.2 Organizing Multiple Sources of Data into One; 2.2.2.1 Merge and dplyr Joins; 2.2.2.1.1 Using merge; 2.2.2.1.2 dplyr; 2.2.3 Cleaning the Data; 2.2.3.1 Correcting Factor Variables; 2.2.3.2 Dealing with NAs; 2.2.3.3 Dealing with Dates and Times; 2.2.3.3.1 Time Zone; 2.2.3.3.2 Daylight Savings Time; 2.2.4 Supplementing with More Information; 2.2.4.1 Derived Variables; 2.2.4.2 n-day Averages; 2.2.5 Reshaping; 2.3 Exploratory Data Analysis; 2.3.1 Summary Statistics; 2.3.1.1 Quantile; 2.3.1.2 Mean; 2.3.1.3 Frequency Plot; 2.3.1.4 Boxplot; 2.3.2 Moment. 2.3.2.1 Variance2.3.2.2 Skewness; 2.3.2.3 Kurtosis; 2.4 Case Study: Credit Card Fraud; 2.4.1 Data Import; 2.4.2 Data Transformation; 2.4.3 Data Exploration; 2.5 Summary; 2.6 References; Chapter 3: Sampling and Resampling Techniques; 3.1 Introduction to Sampling; 3.2 Sampling Terminology; 3.2.1 Sample; 3.2.2 Sampling Distribution; 3.2.3 Population Mean and Variance; 3.2.4 Sample Mean and Variance; 3.2.5 Pooled Mean and Variance; 3.2.6 Sample Point; 3.2.7 Sampling Error; 3.2.8 Sampling Fraction; 3.2.9 Sampling Bias; 3.2.10 Sampling Without Replacement (SWOR).

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