Index
B
- ballistic flight analysis
- about / Ballistic flight analysis – non-linear model, Analysis
- least squares method, using / Analysis by using the least squares method in Python
- Bayes' theorem
- basic application, building / Medical tests – basic application of Bayes' theorem
- analysis / Analysis
- extension / Bayes' theorem and its extension
- about / Bayes' theorem
- proof / Proof
- using, for continuous random variables / Gender classification – Bayes for continuous random variables, Analysis
- bootstrap aggregating / Overview of random forest construction
- business profits
- prediction, by analyzing trends / Business profits – analyzing trends
- trend, analyzing with least squares method / Analyzing trends using the least squares method in Python
- visualizing / Visualization
- trend line equation / Conclusion
C
- chess game
- analysis / Analysis, Analysis
- dependent events / Playing chess – dependent events
- analyzing, with decision tree / Playing chess – analysis with a decision tree, Analysis, Classification
- clusters
- clusters k
- counts, in semantic context / Document clustering – understanding the number of k clusters in a semantic context, Analysis
D
- data
- rescaling / House ownership – data rescaling, Analysis
- using, as decision tree / Swim preference – representing data using a decision tree
- data inconsistency
- dealing with / Going shopping – dealing with data inconsistencies, Analysis
- overcoming, with randomness / Going shopping – overcoming data inconsistencies with randomness and measuring the level of confidence
- confidence level, measuring / Going shopping – overcoming data inconsistencies with randomness and measuring the level of confidence
E
- electronics shop's sales
- seasonality, analyzing / Electronics shop's sales – analyzing seasonality, Analysis, Analyzing seasonality, Conclusion
- trend, analyzing with least squares method / Analyzing trends using the least squares method in Python
- visualizing / Visualization
- extended Bayes' theorem
- about / Extended Bayes' theorem
- proof / Proof
F
- Fahrenheit and Celsius conversion
- linear regression / Fahrenheit and Celsius conversion – linear regression on perfect data
- analysis / Analysis from first principles
- visualization / Visualization
- flight time duration prediction
- based, on distance / Flight time duration prediction based on distance
- analysis / Analysis
G
- gender classification example
- about / Gender classification – clustering to classify
- analysis / Analysis
- input data / Input data from gender classification
- program output / Program output for gender classification data
- gradient descent algorithm
- about / Gradient descent algorithm and its implementation
- implementation / Implementation
- visualization / Visualization – comparison of the least squares method and the gradient descent algorithm
H
- height – linear regression
- weight prediction / Weight prediction from height – linear regression on real-world data, Analysis
I
- ID3 algorithm
- used, for constructing decision tree / ID3 algorithm – decision tree construction
- about / ID3 algorithm – decision tree construction
- used, for classification / Classifying with a decision tree
- information entropy
- defining / Definition of information entropy
- information theory
- about / Information theory
- gaining process / Information gain
- Italy map example
- k value, selecting / Map of Italy example – choosing the value of k
- analysis / Analysis
K
- k-means clustering algorithm
- about / K-means clustering algorithm
- initial k-centroids, selecting / Picking the initial k-centroids
- centroid, computing / Computing a centroid of a given cluster
- applying, on household income example / Using the k-means clustering algorithm on the household income example
- implementing / Implementation of the k-means clustering algorithm
- k-nearest neighbors algorithm
- implementing / Implementation of the k-nearest neighbors algorithm
- used, for text classification / Text classification – k-NN in higher dimensions
- k clusters
- clustering into / Household incomes – clustering into k clusters
L
- least squares method, Python
- used, for analysis / Analysis using the least squares method in Python
- linear regression
- about / Fahrenheit and Celsius conversion – linear regression on perfect data
- least squares method / Least squares method for linear regression
N
- Naive Bayes classifier
- implementation / Implementation of a Naive Bayes classifier
- non-Euclidean distances
- used, for text classification / Text classification – using non-Euclidean distances
R
- random forest algorithm
- about / Introduction to the random forest algorithm
- overview / Overview of random forest construction
- used, for swim preference analysis / Swim preference – analysis involving a random forest
- analysis / Analysis
- constructing / Random forest construction, Random forest construction
- random decision tree number 0, constructing / Construction of random decision tree number 0
- random decision tree number 1, constructing / Construction of random decision tree number 1
- constructed random forest / Constructed random forest
- implementation / Implementation of the random forest algorithm
- chess example, playing / Playing chess example
- classifying / Classification
- used, for performing analysis / Analysis
S
- swimming preference decision tree
- information gain calculation / Swim preference – information gain calculation
- about / Swim preference – decision tree construction by the ID3 algorithm
- implementation / Implementation
- used, for data sample classification / Classifying a data sample with the swimming preference decision tree
- analysis, with random forest algorithm / Swim preference – analysis involving a random forest
T
- temperature preferences / Mary and her temperature preferences
- text classification
- non-Euclidean distances, using / Text classification – using non-Euclidean distances, Analysis
- k-NN, using / Text classification – k-NN in higher dimensions, Analysis
- tree bagging / Overview of random forest construction