A

1. Actual vs. predicted plot linear model

2. Actual vs. predicted plot quadratic polynomial model

3. Amazon Food Review

4. American Statistical Association (ASA)

5. An Exploratory Technique for Investigating Large Quantities of Categorical Data

6. Apache Pig

7. Apriori

8. Area Under the Curve (AUC)

9. Artificial intelligence (AI)

10. Artificial neural networks (ANN)

    1. architecture

       1. components

       2. linear seperability

       3. MLP

    2. attribute importance

       1. by Garson method

       2. by Olden method

    3. deep learning

       1. applications

       2. architecture

       3. darch for classification

       4. guidelines

       5. hidden layers

       6. multi-layer

       7. multiple linear and non-linear transformations

       8. mxNet image classification

       9. mxNet package

       10. normalized image

       11. volcano picture, image recognition exercise

    4. evolutionary methods

    5. expectation maximization

    6. feed-forward back-propagation

    7. GEP

    8. hidden layer

    9. human cognitive learning

    10. learning algorithms

    11. machine learning

    12. non-parametric methods

    13. particle swarm optimization

    14. perceptron

    15. purchase prediction

    16. sigmoid neuron

    17. simulated annealing

    18. supervised vs . unsupervised neural nets

11. Association rule mining (ARM)

    1. algorithms

    2. apriori

    3. confidence

    4. Eclat

    5. IBCF

    6. item frequency plot

    7. lift

    8. Market Basket data

    9. POS

    10. scarcity visualization

    11. support

    12. transactional data

    13. UBCF

12. Autocorrelation

13. Auto-correlation function (ACF)

14. Automatic grid search optimization

B

1. Back-propagation learning

2. Back-propagation method

3. Back-propagation of errors

4. Bagging

   1. bootstrap aggregating

   2. CART

   3. random forest

5. Bayes formula

6. Bayesian algorithms

7. Bayesian optimization, machine learning models

   1. black box function

   2. Gaussian processes

   3. parameters

   4. random tuning

   5. RMSE, cost and Sigma space

   6. sample t-test

8. Bayes rule

9. Bayes theorem

10. Bias and variance tradeoff

    1. boosting

    2. bootstrap aggregation

    3. bulls eye plot

    4. components

    5. definition

    6. graphical representation

    7. model performance improvements

    8. plot function

    9. random variable

    10. real model prototype

11. Bias-variance decomposition

12. Bivariate plots

    1. actual probability

    2. actual vs. predicted plot

       1. CustomerPropensity

       2. IncomeClass

       3. MembershipPoints

    3. frequency

    4. predicted probability

13. Boosting

14. Bootstrap aggregation

15. Bootstrap sampling

    1. advantages

    2. arguments

    3. coefficient

    4. confidence band

    5. density function

    6. disadvantages

    7. histogram

    8. hypothesis testing

    9. jackknife

    10. jackknife estimate

    11. linear regression model

    12. mean and variance

    13. metric estimation

    14. normal distribution

    15. QQ plot

    16. sampling distribution

    17. t.test()

16. Boxplots

    1. interquartile range

    2. outliers

    3. population

17. Breush-Pagan test

18. Bubble charts

    1. fertility rate vs. life expectancy

    2. GDP per capita vs. life expectancy

19. Business implications of sampling

    1. deciding factors

    2. features

    3. machine learning

    4. methods and interpretation

    5. shortcomings

C

1. C5.0 algorithm

   1. attribute-value description

   2. discrete classes

   3. evaluation

   4. Hunt’s approach

   5. logical classification models

   6. model building

   7. model summary

   8. predefined classes

   9. pruning

   10. purchase prediction dataset

   11. Ross Quinlan’s web page

   12. sufficient data

2. caretEmseble() function

3. Caret package

   1. complex regression and classification problems

   2. function/tools

   3. trainControl() function

   4. train() function algorithm

4. CART

1. Central Limit Theorem

2. Centroid-based clustering

3. Chi-Square Automated Interaction Detection (CHAID)

   1. algorithm

   2. building the model

   3. decision tree

   4. model evaluation

   5. R code

   6. splitting

   7. stopping

4. Classification and Regression Tree (CART)

   1. building the model

   2. cp (complexity parameter)

   3. Gini-Index

   4. model evaluation

   5. pseudo code

   6. regression tree-based approach

   7. rpart function

5. Classification matrix

6. Classification tree

7. Class imbalance

8. Cluster dendogram

9. Cluster sampling

   1. advantages

   2. conditional statement

   3. disadvantages

   4. international transactions

   5. International transactions

   6. k-means function

   7. outstanding balance

   8. population data

   9. single-stage sampling

   10. startum variable

   11. stratified() function

   12. subsets

   13. two-stage sampling

   14. t.test()

   15. two-stage

10. Clustering algorithms

11. Clustering analysis

    1. algorithms

    2. applications

    3. centroid-based clustering

    4. centroid models

    5. connectivity models

    6. definition

    7. density-based clustering

    8. density models

    9. distribution-based clustering

    10. distribution models

    11. Dunn index

    12. external evaluation

    13. hierarchal

    14. internal evaluation

    15. Jaccard index

    16. k-means

    17. machine learning

    18. principle

    19. rand measure

    20. silhouette coefficient

    21. types

    22. unsupervised learning algorithm

12. Cohort diagrams

    1. active credit cards volume

    2. credit example

    3. definition

13. Collaborative filtering-based approach

14. Comma-separated values (CSV)

15. Computational savings

    1. linear regression model

    2. population dataset

    3. sys.time()

16. Conditional independence

17. Confidence interval

18. Continuous variables

19. Convenience sampling

20. Cook’s distance

21. Correlation, definition

22. Correlation analysis

    1. features

    2. observations

    3. Pearson correlation

    4. population correlation coefficient

    5. scatter plot, HousePrice vs. StoreArea

    6. statistical relationship

23. Correlation plots

    1. description

    2. positive or negative correlation

    3. world development indicators

24. Credit card fraud

    1. data description

    2. data exploration

    3. data import

    4. data transformation

    5. pooled mean and variance

    6. population mean

    7. population variance

    8. sampling plan

    9. statistical measures

25. Credit risk modeling

26. Custom search algorithms

D

1. Data formats

2. Data frames

3. Data mining

4. Data preparation and exploration

   1. categorical variables

   2. data and visualization

   3. date variable

   4. derived variables

   5. markup language

   6. model building

   7. n-day averages

   8. reshaping

   9. semi-Structured

   10. structured

   11. unstructured

   12. variables types

5. Data science

6. Dataset

   1. house sale prices prediction

   2. purchase preference prediction

7. Data visualization, R

8. Data visualization, R

   1. benefits

   2. boxplots

   3. bubble charts

   4. cohort diagrams

   5. correlation plots

   6. definition

   7. dendograms

   8. elements, data presentation

   9. ggplot2 package

   10. heatmaps

   11. histograms and density plots

   12. line chart

   13. pie charts

   14. Sankey plots

   15. scatterplot

   16. spatial maps

   17. stacked column charts

   18. time series graphs

   19. waterfall chart

   20. wordclouds

   21. world development indicators

9. Dates and times

10. Daylight saving time (DST)

11. Decision trees

    1. algorithms

    2. bagging

    3. boosting

    4. classification

    5. decision nodes

    6. ensemble models

    7. ID3

    8. leaf nodes

    9. learning methods

    10. measures

        1. entropy

        2. Gini Index

        3. information gain

    11. non-parametric model

    12. regression

12. Deep learning algorithms

13. Dendograms

    1. clusters, species classification

    2. definition

    3. distance/height

    4. ggdendro() and dendextend()

    5. x-axis

    6. y-axis

14. Density-based clustering

    1. border points

    2. core points

    3. DBSCAN

    4. EM algorithm

    5. outliers

    6. parameters

15. Density-based spatial clustering of applications with noise (DBSCAN)

16. Density plot

17. Dimensionality reduction

    1. algorithms

    2. description

    3. orthogonality, principal components

    4. PCA

    5. principal component analysis

18. Directed Acyclic Graph (DAG)

19. Distance-based/event-based algorithms

20. Distributed processing and storage

    1. GFS

    2. MapReduce

    3. parallel execution in R

       1. cores setting

       2. problem statement

       3. random forest model

       4. stopping clusters

21. Distribution-based clustering

22. Distribution of studentized residuals

23. dplyr

24. Dunn Index

25. Durbin Watson statistics bounds

26. Durbin Watson test

E

1. Eclat

2. EM algorithm

3. Empirical Distribution Function (EDF)

4. Ensemble learning

   1. methods

      1. bagging

      2. boosting

   2. model performance improvement

   3. supervised learning algorithm

   4. voting ensembles

5. Ensemble models

6. Ensemble techniques illustration, R

   1. algorithms, purchase prediction data

   2. bagging trees

   3. blending KNN and Rpart

   4. C5.0 decision tree model

   5. Caret package

   6. caretStack() function

   7. GBM model

   8. resamples() function

   9. stacking, caretEnsemble

7. Entropy

8. Exploratory Data Analysis (EDA)

9. Exposure at Default (EAD)

10. Extensible Markup languages (XML)

F

1. Factor variables

2. False positive rate (FPR)

3. Feature engineering

   1. checklist

   2. dimensionality reduction

1. embedded methods

2. feature ranking

3. filter methods

4. selection problem checklist

5. variable subset selection

1. working data

   1. continuous/categorical features

   2. EAD

   3. LGD

   4. PD

   5. willingness to pay and ability to pay

2. wrapper methods

1. Feature ranking

2. Feedforward Neural Networks (FFNN)

3. Fine needle aspirate (FNA)

4. Fuzzy C-means clustering

G

1. Gains charts, AUC

2. Gauss-Markov theorem

3. Gene expression programming (GEP)

4. Generalized Linear Model (GLM)

5. GFS

1. ggplot2 Package

   1. description

   2. R documentation

2. Gini-Index

3. Google file system (GFS)

4. Gradient Boosting Machine (GBM)

H

1. H2O, machine learning in R

   1. clusters initialization

   2. deep learning demo

   3. documented materials

   4. java virtual machine

   5. package installation

   6. running demo

   7. testing data

2. Hadoop ecosystem

   1. Apache Pig

      1. command pig-x local connects

      2. count and sort

      3. flattening tokens

      4. group words

      5. load data into A1

      6. tokenize each line

   2. components and tools

   3. hadoop distributed file system

   4. Hadoop YARN

   5. HBase

      1. create and put data

      2. data scanning

      3. starting HBase

   6. Hive

      1. Apache

      2. creating tables

      3. data loading, Hive table

      4. describing tables

      5. generating data and storing

      6. HDFS

      7. large-scale data processing

      8. query selection

      9. SQL queries

   7. MapReduce

      1. code snippet

      2. libraries rmr2 and rhdfs

      3. procedures

      4. shuffle

      5. Word Count

      6. wordcount function

   8. spark

3. Heat maps

   1. description

   2. regions vs. world development indicators

4. Hierarchal clustering

5. Hinge loss

6. Histogram

   1. construction

   2. description

   3. GDP and population

7. Homoscedasticity

8. House sale price dataset

9. Human cognitive learning

10. Hyper-parameters

    1. Bayesian approach

    2. decision points

    3. “higher-level” properties

    4. optimization

       1. automatic grid search

       2. custom search algorithms

       3. manual grid search

       4. manual search

       5. optimal search

       6. random search

    5. properties

    6. random forest algorithm

    7. random forest models

11. Hypertext Markup Language (HTML)

12. Hypothesis testing

I

1. Independent events

2. Influence plot

3. Infographics

4. Information gain

5. Initial data analysis (IDA)

   1. description

   2. dplyr

   3. multiple sources

   4. naming convention

   5. str() function

   6. table(): pattern

6. Item-Based Collaborative Filtering (IBCF)

   1. cosine/Pearson correlation

   2. creation rating matrix

   3. data preparation

   4. distribution of ratings

   5. evaluation

   6. exploring, rating matrix

   7. loading data

   8. raw ratings by users

   9. true positive ratio vs. false positive ratio

   10. UBCF recommendation model

7. Iteration error

8. Iterative Dichotomizer 3 (ID3)

   1. algorithm

   2. commands

   3. model building

   4. model evaluation

   5. RWeka

   6. RWekajars

J

1. Jaccard index

2. JSON file

K

1. Kappa error metric

2. K-fold cross validation

3. K-Means Clustering Algorithm

4. Knowledge Discovery and Data Mining (KDD)

5. Kolmogorov-Smirnov tests (KS test)

6. Kurtosis

L

1. Law of Large Numbers (LLN)

   1. strong law

   2. weak law

2. Learning Vector Quantization (LVQ)

3. Least Absolute Shrinkage and Selection Operator (LASSO)

4. LGD

1. Lift chart

2. Linear predictors

   1. bias of estimator

   2. consistent estimator

   3. efficient estimator

   4. OLS

3. Linear regression

   1. actual vs. predicted

   2. affine function

   3. definition

   4. dependent and independent variable

   5. diagnostics

   6. estimated equation

   7. estimation

   8. Gauss-Markov theorem

   9. lm() package

   10. minimization problem

   11. model diagnostics

       1. homoscedasticity

       2. influential point analysis

       3. multicollinearity

       4. normality of residuals

       5. outliers

       6. residual autocorrelation

   12. OLS

   13. parametric method

   14. predicted values

   15. residuals

   16. standard error

   17. t-value and p-value

4. Line chart

   1. description

   2. GDP growth, countries

   3. melt() function

5. Link function

6. List

7. Logistic regression

   1. analysis

   2. binomial

   3. binomially distributed

   4. logit transformation

   5. model diagnostics

      1. bivariate plots

      2. concordance and discordant ratios

      3. cumulative gains and lift charts

      4. deviance

      5. log likelihoods

      6. pseudo R-Square

      7. wald test

   6. multinomial

   7. odds ratio

   8. ordered

   9. predictor variables

8. Logit function

9. Logit transformation

10. Loss Given Default (LGD)

11. LOWESS plot (Locally Weighted Scatterplot Smoothing)

M

1. Machine learning (ML)

   1. abstraction layer

   2. algorithms

      1. ANN

      2. association rule mining

      3. Bayesian algorithms

      4. clustering algorithms

      5. deep learning

      6. dimensionality reduction

      7. distance-based/event-based algorithms

      8. ensemble learning

      9. regression-based methods

      10. regularization methods

      11. text mining

      12. tree-based algorithms

   3. case study

   4. computer vision

   5. 3D approach

      1. demo in R

      2. real-world use case

      3. statistical background

   6. distributions

   7. evaluation

   8. exploration

   9. feature engineering

1. friction-less pipeline

2. intelligent personal assistant/machines

3. PEBE framework

4. phase forms

5. plethora of algorithms

6. predictive models

7. process flow

8. probability

   1. conditional independence

   2. counting

   3. independent events

   4. notation

   5. statistics

9. randomness

10. R-package

11. statistical concepts

12. statistical learning

13. statistical modeling

14. statistics and computer science

15. types

    1. factors

    2. reinforcement learning

    3. semi-supervised learning

    4. supervised learning

    5. unsupervised learning

1. Manual grid search optimization

2. MapReduce

3. Market Basket Data

4. Matrix

5. Maximum likelihood estimation (MLE)

6. Mean

7. Mean absolute error

8. Mean Absolute Percentage Error (MAPE)

9. Mean Absolute Scaled Error (MASE)

10. Microsoft Excel

11. Model building checklist

12. Model evaluation

    1. continuous output

       1. mean absolute error

       2. model performance metrics

       3. RMSE

       4. R-square

    2. discrete output

       1. classification matrix

       2. ROC curve

       3. sensitivity and specificity

    3. kappa error metric

    4. population stability index

1. probabilistic techniques

1. statistical methods

1. Model performance

   1. Bayesian optimization

   2. bias and variance tradeoff

1. Caret package

2. continuous output

3. discrete output

4. ensemble learning

1. evaluation

2. hyper-parameters

1. machine learning and statistical modeling

2. testing data

3. training data

4. validation data

1. Model performance

1. Model sampling

2. Model-selection process

3. Model suffering

   1. from bias

   2. from variance

4. Moment

5. Monte Carlo method

   1. acceptance-rejection methods

   2. beta density

   3. EDF

   4. random sampling techniques

   5. stochastic calculus

6. Multicollinearity

7. Multi-Layer Perceptron (MLP)

8. Multinomial logistic regression

   1. classifier

   2. class imbalance

   3. estimation process

   4. multinom() function

   5. probability/proportion

N

1. Naive Bayes method

   1. Bayes theorem

   2. chain rule

   3. conditional probability

   4. data preparation

   5. likelihood and marginal likelihood

   6. model

   7. model evaluation

   8. posterior probability

   9. prior probability

   10. purchase prediction dataset

2. National Sample Survey Organization (NNSO)

3. Natural Language Processing (NLP)

4. Neuron anatomy

5. Nonparametric Multiplicative Regression (NPMR)

6. Non-probability sampling

7. Not Available (NAs)

O

1. Online machine learning algorithms

   1. benefits and challenges

   2. fuzzy C-means clustering

   3. tackling

2. Optimal search optimization

3. Ordinary Least Square (OLS)

P

1. Particle swarm optimization

2. Part-of-speech (POS)

   1. categorization

   2. extraction

   3. frequency

   4. mapping

   5. pre-processing

3. Pearson Product-Moment Correlation Coefficient

4. Perceptron

5. Performance evaluation metrics

6. Permutation

7. Pie charts

8. Point-of-sale (POS)

9. Polynomial regression

10. Pooled mean

11. Pooled variance

12. Population stability index

    1. continuous distribution

    2. discrete cases

    3. discrete distributions

    4. ECDF plots, Set_1 and Set_2

    5. Empirical Cumulative Distribution Function (ECDF)

    6. KS test

    7. threshold values

13. Principal component analysis (PCA)

    1. advantages

    2. orthogonality

    3. steps

14. Probabilistic techniques

    1. bootstrap sampling

    2. K-fold cross validation

15. Probability

    1. vs. non-probability sampling

    2. sampling technique

       1. data dimensions

       2. histogram

       3. population mean

       4. population variance

       5. sampling methods

16. Probability of default (PD)

17. Pseudo R-Square

18. Purposive sampling

Q

1. Quantile

2. Quota sampling

R

1. R

   1. building blocks

   2. calculations

   3. data frames

   4. data structures

   5. functions

   6. GNU S

   7. lists

   8. matrixes

   9. packages

   10. statistics

   11. subsetting

   12. vectors

2. Radial basis function (RBF)

3. Rand index

4. Random Forest

5. Random search algorithms

6. Random search optimization

7. rbinom()

8. R code

9. Receiver operating characteristic (ROC) curve

10. Recommendation algorithm

11. Recursive binary split

12. Recursive partitioning

13. Regression analysis

    1. causation

    2. distributional assumptions

    3. linear model

    4. non-parametric methods

    5. notation

    6. parametric methods

    7. prediction/forecasting

    8. statistical learning and machine learning space

    9. statistical model

    10. variables

14. Regression-based methods

15. Regression trees

16. Regularization algorithms

17. Reinforcement learning

18. Relational Database Management Systems (RDBMS)

19. Residual Sum of Squares (RSS)

20. Residuals vs. fitted plot

21. River plots

1. RMSE

1. ROC curve

1. Root mean square error (RMSE)

2. Root node

S

1. Sample point

2. Sampling

   1. bias

   2. classification

   3. description

   4. distribution

   5. error

   6. fraction

   7. objectives

   8. population mean

   9. population statistics

   10. sources and storing

   11. technological advancement

   12. test statistics

   13. variance

3. Sampling without replacement (SWOR)

4. Sampling with replacement (SWR)

5. Sankey plots

6. Scatterplots

   1. description

   2. higher dimensional

   3. population vs. GDP relationship

7. Semi-supervised learning

8. Serial correlation

9. Shapiro-Wilk test

10. Sigmoid function

11. Sigmoid neurons

12. Silhouette coefficient

13. Simple random sampling

    1. distribution of data

    2. function

    3. histograms

    4. hypothesis

    5. KS test

    6. population

    7. population average

    8. population sampling

    9. population size

    10. p-value of t.test

    11. replacement

    12. sample and population

    13. sample() function

    14. summarise function

    15. without replacement

14. Simulated annealing

15. Simulation

16. Skewness

17. Spark’s machine learning

    1. algorithms

    2. build, ML model

    3. MLlib

    4. preprocessing

    5. SparkDataFrame creation

    6. SparkR session, initializing

    7. sparkR.stop()

    8. system properties, setting

    9. test dataset

    10. tools

18. Spatial maps

    1. data frame creation

    2. ggmap()

    3. ggplot() function

    4. India map, robbery counts

19. Specialization vs. generalization

20. Squared Euclidean distance

21. Stacked column charts

    1. age dependency ratio

    2. contribution, sectors

    3. description

    4. working age ratio

22. Stacking

23. Statistical learning

24. Stratified random sampling

    1. disadvantages

    2. histograms

    3. KS test

    4. population

    5. proportion

    6. sample() function

    7. stratified function

    8. stratified sampling

    9. stratum variables

    10. sub-populations

    11. summarise() function

    12. t.test()

25. Summary statistics

26. Supervised learning

27. Supervised vs. unsupervised learning

28. Support vector machine (SVM)

    1. binary classifier

       1. data preparation

       2. data summary

       3. model building

       4. model evaluation

    2. classification

    3. class separation

    4. hard margins

    5. linear

    6. multi-class

    7. nonlinearity

    8. overlapping classes

    9. soft margins

29. Systematic random sampling

    1. business and computational capacity

    2. circular sampling frame

    3. EDF

    4. formula

    5. homogeneous sets

    6. KS test

    7. population variance

    8. sample distribution

    9. sample frame

    10. skip factor

    11. subsetting

T

1. Term Frequency/Inverse Term frequency (TF_IDF)

2. Text mining algorithms

3. Text-mining approaches

   1. consumer behavior/product performance

   2. data preparation

   3. data summary

   4. Microsoft Cognitive Services

      1. analytics features

      2. language detection

      3. mscstexta4r

      4. Project Oxford

      5. sentiment analysis

      6. summarization

      7. third-party API

      8. topic detection

      9. twitterR() package

   5. NLP

   6. POS tagging

   7. summarization

   8. text analysis

   9. text data

   10. TF-IDF

   11. Twitter statics

   12. word cloud

4. Time series graphs

   1. GDP growth, countries

   2. GDP growth, recession

5. Torsten Hothorn

6. True Negative Rate (TNR)

7. True positive rate (TPR)

8. Twitter feeds and article

U

1. UCI Machine Learning Repository

2. Unsupervised Fuzzy Competitive Learning

3. Unsupervised learning

4. User-Based Collaborative Filtering (UBCF)

V

1. Variable subset selection

   1. definition

   2. embedded method

      1. fit model

      2. fitted Cross Validated Linear Model

      3. glmnet fit model

      4. logistic regression

      5. misclassification error and log of penalization factor (lambda)

      6. regularization

      7. statistical approaches

   3. filter method

      1. CoV

      2. Gini coefficient

      3. statistical approaches

      4. variance threshold

   4. wrapper method

2. Variance

3. Variance inflation factor (VIF)

4. Vectors

W

1. Wald test

2. Waterfall charts

3. Within cluster sum of squares (WCSS)

4. Wordclouds

5. World development indicators (WDI)

X, Y, Z

1. XML