Data science algorithms in a week : top 7 algorithms for scientific computing, data analysis, and machine learning

Data science algorithms in a week top 7 algorithms for scientific computing, data analysis, and machine learning

Build a strong foundation of machine learning algorithms in 7 days Key Features Use Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a week Know when and where to apply data science algori...

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Detalles Bibliográficos
Otros Autores: Natingga, David, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham : Packt 2018.
Edición:Second edition
Materias:
Machine learning.
Computer algorithms.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631823806719
Tabla de Contenidos:
  • Intro
  • Title Page
  • Copyright and Credits
  • Packt Upsell
  • Contributors
  • Table of Contents
  • Preface
  • Classification Using K-Nearest Neighbors
  • Mary and her temperature preferences
  • Implementation of the k-nearest neighbors algorithm
  • Map of Italy example - choosing the value of k
  • Analysis
  • House ownership - data rescaling
  • Analysis
  • Text classification - using non-Euclidean distances
  • Analysis
  • Text classification - k-NN in higher dimensions
  • Analysis
  • Summary
  • Problems
  • Mary and her temperature preference problems
  • Map of Italy - choosing the value of k
  • House ownership
  • Analysis
  • Naive Bayes
  • Medical tests - basic application of Bayes' theorem
  • Analysis
  • Bayes' theorem and its extension
  • Bayes' theorem
  • Proof
  • Extended Bayes' theorem
  • Proof
  • Playing chess - independent events
  • Analysis
  • Implementation of a Naive Bayes classifier
  • Playing chess - dependent events
  • Analysis
  • Gender classification - Bayes for continuous random variables
  • Analysis
  • Summary
  • Problems
  • Analysis
  • Decision Trees
  • Swim preference - representing data using a 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 the ID3 algorithm
  • Implementation
  • Classifying with a decision tree
  • Classifying a data sample with the swimming preference decision tree
  • Playing chess - analysis with a decision tree
  • Analysis
  • Classification
  • Going shopping - dealing with data inconsistencies
  • Analysis
  • Summary
  • Problems
  • Analysis
  • Random Forests
  • Introduction to the random forest algorithm
  • Overview of random forest construction.
  • Swim preference - analysis involving a random forest
  • Analysis
  • Random forest construction
  • Construction of random decision tree number 0
  • Construction of random decision tree number 1
  • Constructed random forest
  • Classification using random forest
  • Implementation of the random forest algorithm
  • Playing chess example
  • Analysis
  • Random forest construction
  • Classification
  • Going shopping - overcoming data inconsistencies with randomness and measuring the level of confidence
  • Analysis
  • Summary
  • Problems
  • Analysis
  • 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
  • Using the k-means clustering algorithm on the household income example
  • Gender classification - clustering to classify
  • Analysis
  • Implementation of the k-means clustering algorithm
  • Input data from gender classification
  • Program output for gender classification data
  • House ownership - choosing the number of clusters
  • Analysis
  • Document clustering - understanding the number of k clusters in a semantic context
  • Analysis
  • Summary
  • Problems
  • Analysis
  • Regression
  • Fahrenheit and Celsius conversion - linear regression on perfect data
  • Analysis from first principles
  • Least squares method for linear regression
  • Analysis using the least squares method in Python
  • Visualization
  • Weight prediction from height - linear regression on real-world data
  • Analysis
  • Gradient descent algorithm and its implementation
  • Gradient descent algorithm
  • Implementation
  • Visualization - comparison of the least squares method and the gradient descent algorithm
  • Flight time duration prediction based on distance
  • Analysis
  • Ballistic flight analysis - non-linear model
  • Analysis.
  • Analysis by using the least squares method in Python
  • Summary
  • Problems
  • Analysis
  • Time Series Analysis
  • Business profits - analyzing trends
  • Analysis
  • Analyzing trends using the least squares method in Python
  • Visualization
  • Conclusion
  • Electronics shop's sales - analyzing seasonality
  • Analysis
  • Analyzing trends using the least squares method in Python
  • Visualization
  • Analyzing seasonality
  • Conclusion
  • Summary
  • Problems
  • Analysis
  • Python Reference
  • Introduction
  • Python Hello World example
  • Comments
  • Data types
  • int
  • float
  • String
  • Tuple
  • List
  • Set
  • Dictionary
  • Flow control
  • Conditionals
  • For loop
  • For loop on range
  • For loop on list
  • Break and continue
  • Functions
  • Input and output
  • Program arguments
  • Reading and writing a file
  • Statistics
  • Basic concepts
  • Bayesian inference
  • Distributions
  • Normal distribution
  • Cross-validation
  • K-fold cross-validation
  • A/B testing
  • Glossary of Algorithms and Methods in Data Science
  • Other Books You May Enjoy
  • Index.

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