Pro machine learning algorithms : a hands-on approach to implementing algorithms in Python and R, V. Kishore Ayyadevara

Resource Information

The instance Pro machine learning algorithms : a hands-on approach to implementing algorithms in Python and R, V. Kishore Ayyadevara represents a material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool. This resource is a combination of several types including: Instance, Electronic.

Image

http://covers.openlibrary.org/b/isbn/9781484235645-M.jpg?default=false

Label

Pro machine learning algorithms : a hands-on approach to implementing algorithms in Python and R, V. Kishore Ayyadevara

Title remainder

a hands-on approach to implementing algorithms in Python and R

Statement of responsibility

V. Kishore Ayyadevara

Instantiates

• Pro machine learning algorithms : a hands-on approach to implementing algorithms in Python and R

Publication

• Berkeley, Apress, 2018

Antecedent source

unknown

Carrier category

online resource

Carrier category code

• cr

Carrier MARC source

rdacarrier

Color

multicolored

Content category

text

Content type code

• txt

Content type MARC source

rdacontent

Contents

• Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Basics of Machine Learning; Regression and Classification; Training and Testing Data; The Need for Validation Dataset; Measures of Accuracy; Absolute Error; Root Mean Square Error; Confusion Matrix; AUC Value and ROC Curve; Unsupervised Learning; Typical Approach Towards Building a Model; Where Is the Data Fetched From?; Which Data Needs to Be Fetched?; Pre-processing the Data; Feature Interaction; Feature Generation; Building the Models; Productionalizing the Models
• Build, Deploy, Test, and IterateSummary; Chapter 2: Linear Regression; Introducing Linear Regression; Variables: Dependent and Independent; Correlation; Causation; Simple vs. Multivariate Linear Regression; Formalizing Simple Linear Regression; The Bias Term; The Slope; Solving a Simple Linear Regression; More General Way of Solving a Simple Linear Regression; Minimizing the Overall Sum of Squared Error; Solving the Formula; Working Details of Simple Linear Regression; Complicating Simple Linear Regression a Little; Arriving at Optimal Coefficient Values; Introducing Root Mean Squared Error
• Running a Simple Linear Regression in RResiduals; Coefficients; SSE of Residuals (Residual Deviance); Null Deviance; R Squared; F-statistic; Running a Simple Linear Regression in Python; Common Pitfalls of Simple Linear Regression; Multivariate Linear Regression; Working details of Multivariate Linear Regression; Multivariate Linear Regression in R; Multivariate Linear Regression in Python; Issue of Having a Non-significant Variable in the Model; Issue of Multicollinearity; Mathematical Intuition of Multicollinearity; Further Points to Consider in Multivariate Linear Regression
• Assumptions of Linear RegressionSummary; Chapter 3: Logistic Regression; Why Does Linear Regression Fail for Discrete Outcomes?; A More General Solution: Sigmoid Curve; Formalizing the Sigmoid Curve (Sigmoid Activation); From Sigmoid Curve to Logistic Regression; Interpreting the Logistic Regression; Working Details of Logistic Regression; Estimating Error; Scenario 1; Scenario 2; Least Squares Method and Assumption of Linearity; Running a Logistic Regression in R; Running a Logistic Regression in Python; Identifying the Measure of Interest; Common Pitfalls
• Time Between Prediction and the Event HappeningOutliers in Independent variables; Summary; Chapter 4: Decision Tree; Components of a Decision Tree; Classification Decision Tree When There Are Multiple Discrete Independent Variables; Information Gain; Calculating Uncertainty: Entropy; Calculating Information Gain; Uncertainty in the Original Dataset; Measuring the Improvement in Uncertainty; Which Distinct Values Go to the Left and Right Nodes; Gini Impurity; Splitting Sub-nodes Further; When Does the Splitting Process Stop?; Classification Decision Tree for Continuous Independent Variables

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unknown

Extent

1 online resource

File format

unknown

Form of item

online

Isbn

9781484235645

Level of compression

unknown

Media category

computer

Media MARC source

rdamedia

Media type code

• c

Quality assurance targets

not applicable

Record ID

b5439547

Reformatting quality

unknown

Sound

unknown sound

Specific material designation

remote

System control number

• on1042561229
• (OCoLC)1042561229