Data science algorithms in a week : data analysis, machine learning, and more / Dávid Natingga

By:

Natingga, Dv̀id [author]

Publication details: Birmingham, UK : Packt Publishing, 2017.Description: iv, 197 pages : illustration ; 24 cmISBN:

9781787284586

Subject(s):

DDC classification:

23 006.31 N212D

Contents:

Cover ; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Classification Using K Nearest Neighbors ; Mary and her temperature preferences; Implementation of k-nearest neighbors algorithm; Map of Italy example -- choosing the value of k; House ownership -- data rescaling; Text classification -- using non-Euclidean distances; Text classification -- k-NN in higher-dimensions; Summary; Problems; Chapter 2: Naive Bayes ; Medical test -- basic application of Bayes' theorem; Proof of Bayes' theorem and its extension. Extended Bayes' theoremPlaying chess -- independent events; Implementation of naive Bayes classifier; Playing chess -- dependent events; Gender classification -- Bayes for continuous random variables; Summary; Problems; Chapter 3: Decision Trees ; Swim preference -- representing data with decision tree; Information theory; Information entropy; Coin flipping; Definition of information entropy; Information gain; Swim preference -- information gain calculation; ID3 algorithm -- decision tree construction; Swim preference -- decision tree construction by ID3 algorithm; Implementation. Classifying with a decision treeClassifying a data sample with the swimming preference decision tree; Playing chess -- analysis with decision tree; Going shopping -- dealing with data inconsistency; Summary; Problems; Chapter 4 : Random Forest; Overview of random forest algorithm; Overview of random forest construction; Swim preference -- analysis with random forest; Random forest construction; Construction of random decision tree number 0; Construction of random decision tree number 1; Classification with random forest; Implementation of random forest algorithm; Playing chess example. Random forest constructionConstruction of a random decision tree number 0:; Construction of a random decision tree number 1, 2, 3; Going shopping -- overcoming data inconsistency with randomness and measuring the level of confidence; Summary; Problems; Chapter 5: Clustering into K Clusters ; Household incomes -- clustering into k clusters; K-means clustering algorithm; Picking the initial k-centroids; Computing a centroid of a given cluster; k-means clustering algorithm on household income example; Gender classification -- clustering to classify. Implementation of the k-means clustering algorithmInput data from gender classification; Program output for gender classification data; House ownership -- choosing the number of clusters; Document clustering -- understanding the number of clusters k in a semantic context; Summary; Problems; Chapter 6: Regression ; Fahrenheit and Celsius conversion -- linear regression on perfect data; Weight prediction from height -- linear regression on real-world data; Gradient descent algorithm and its implementation; Gradient descent algorithm. Visualization -- comparison of models by R and gradient descent algorithm.

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Holdings
Item type	Current library	Call number	Status	Date due	Barcode
Books	Library and Documentation Division PGRRL	006.31 N212D (Browse shelf(Opens below))	Available		111894

Cover ; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Classification Using K Nearest Neighbors ; Mary and her temperature preferences; Implementation of k-nearest neighbors algorithm; Map of Italy example --
choosing the value of k; House ownership --
data rescaling; Text classification --
using non-Euclidean distances; Text classification --
k-NN in higher-dimensions; Summary; Problems; Chapter 2: Naive Bayes ; Medical test --
basic application of Bayes' theorem; Proof of Bayes' theorem and its extension. Extended Bayes' theoremPlaying chess --
independent events; Implementation of naive Bayes classifier; Playing chess --
dependent events; Gender classification --
Bayes for continuous random variables; Summary; Problems; Chapter 3: Decision Trees ; Swim preference --
representing data with decision tree; Information theory; Information entropy; Coin flipping; Definition of information entropy; Information gain; Swim preference --
information gain calculation; ID3 algorithm --
decision tree construction; Swim preference --
decision tree construction by ID3 algorithm; Implementation. Classifying with a decision treeClassifying a data sample with the swimming preference decision tree; Playing chess --
analysis with decision tree; Going shopping --
dealing with data inconsistency; Summary; Problems; Chapter 4 : Random Forest; Overview of random forest algorithm; Overview of random forest construction; Swim preference --
analysis with random forest; Random forest construction; Construction of random decision tree number 0; Construction of random decision tree number 1; Classification with random forest; Implementation of random forest algorithm; Playing chess example. Random forest constructionConstruction of a random decision tree number 0:; Construction of a random decision tree number 1, 2, 3; Going shopping --
overcoming data inconsistency with randomness and measuring the level of confidence; Summary; Problems; Chapter 5: Clustering into K Clusters ; Household incomes --
clustering into k clusters; K-means clustering algorithm; Picking the initial k-centroids; Computing a centroid of a given cluster; k-means clustering algorithm on household income example; Gender classification --
clustering to classify. Implementation of the k-means clustering algorithmInput data from gender classification; Program output for gender classification data; House ownership --
choosing the number of clusters; Document clustering --
understanding the number of clusters k in a semantic context; Summary; Problems; Chapter 6: Regression ; Fahrenheit and Celsius conversion --
linear regression on perfect data; Weight prediction from height --
linear regression on real-world data; Gradient descent algorithm and its implementation; Gradient descent algorithm. Visualization --
comparison of models by R and gradient descent algorithm.

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