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

Build strong foundation of machine learning algorithms In 7 days. About This Book Get to know seven algorithms for your data science needs in this concise, insightful guide Ensure you're confident in the basics by learning when and where to use various data science algorithms Learn to use machi...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Natingga, Dávid (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing, 2017.
Temas:
Machine learning.
Computer algorithms.
Algorithms.
Apprentissage automatique.
Algorithmes.
algorithms.
COMPUTERS > General.
Algorithms
Computer algorithms
Machine learning
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

Ejemplares similares