Index

Symbols

A

actions 453, 454

action-value 473

activation function 256

activation functions

linear 268

ReLU 268

sigmoid 268

softmax 268

tanh 268

ad click-through

predicting, with logistic regression 165, 166

ad click-through prediction 110

with decision tree 134, 136, 137, 138, 139, 140

adjusted R² 245

agent 453, 454

AI-based assistance 4

AI plus human intelligence 4

AlphaGo 3

Anaconda 38

reference link 37

Apache Hadoop

URL 355

Arcene Dataset 97

area under the curve (AUC) 68

Artificial General Intelligence (AGI) 452

Artificial Intelligence (AI) 8

artificial masterpieces, Google Arts & Culture

reference link 261

artificial neural networks (ANNs) 11, 254

association 315

attributes 315

automation

versus machine learning 5

averaging 32

B

backpropagation 258, 259

Backpropagation Through Time (BPTT) 420

bagging 32, 140

bag of words (BoW) 362

Bag of Words (BoW) model 301

basic linear algebra

reference link 8

Bayes 48

Bayes' theorem

example 49, 50, 51

Bellman optimality equation

reference link 460

bias 14, 154, 227

bias-variance trade-off 17, 18

Bidirectional Encoder Representations from Transformers (BERT) 448

bigrams 289

binarization 360

binary classification 45, 268

binning 31

Blackjack environment

reference link 469

simulating 468, 470

boosting 34, 36, 142

bootstrap aggregating 140

bootstrapping 32

Box-Cox transformation 31

C

C4.5 116

categorical features 111, 112

converting, to numerical features 148, 150, 151

categorical variables

combining 207, 209, 210

categories 44

chain rule 259

Chebyshev distance 316

Chi-squared Automatic Interaction Detector (CHAID) 116

classes 44

classification 10, 44

binary classification 45

multiclass classification 46, 47

multi-label classification 47, 48

Classification and Regression Tree (CART) 116

classification performance

evaluating 65, 66, 67, 68, 70

click-through rate (CTR) 110

clothing Fashion-MNIST

reference link 388

clothing image classifier

improving, with data augmentation 406, 407, 408, 409

clothing image dataset 388, 389, 391

clothing images, classifying with CNNs 392

CNN model, architecting 392, 393, 394

CNN model, fitting 395, 396, 397, 398

convolutional filters, visualizing 398, 399, 400

clustering 315

CNN 382

architecting, for classification 387, 388

convolutional layer 382, 383, 384

nonlinear layer 384

pooling layer 385, 386

CNN classifier

boosting, with data augmentation 400

coefficients 153, 227

color restoration 261

computation graphs 40

computer vision 260

conda 37

confusion matrix 66

Continuous Bag of Words (CBOW) 363

convex function 154

reference link 155

convolutional layer 382, 383, 384

Corpora 287, 288, 289

cost function 9, 155, 157, 158

Cross-Industry Standard Process for Data Mining (CRISP-DM) 25

business understanding 26

data preparation 26

data understanding 26

deployment phase 26

evaluation phase 26

modeling phase 26

URL 25

cross-validation

used, for avoiding overfitting 19, 20, 21

used, for tuning models 70, 72, 73

cumulative rewards 455

D

data

acquiring 222, 223, 224, 225, 226

classifying, with logistic regression 151

data augmentation

clothing image classifier, improving 406, 407, 408, 409

CNN classifier, boosting 400

DataFrames 185

data preparation stage

best practices 349, 350, 351, 352, 353, 354, 355

data preprocessing 355

data technology (DT) 6

decision hyperplane 78

decision tree

ad click-through prediction 134, 136, 137, 138, 139, 140

constructing 115, 116

ensembling 140, 142, 143, 144, 145

exploring 112, 113, 114

implementing 124, 125, 127, 128, 129, 131, 132

implementing, with scikit-learn 133, 134

decision tree module

reference link 133

decision tree regression

estimating with 234

implementing 237, 238, 240, 241

decoder 446

deep learning 11

deep learning (DL) 254

deep neural networks 30

deployment and monitoring stage

best practices 374, 375, 376, 377, 378

dimensionality reduction 25, 307, 308

used, for avoiding overfitting 24

discretization 361

distributed computing 294

document frequency 334

Dorothea Dataset 97

dot product 382

Dow Jones Industrial Average (DJIA) 217

downsampling layer 385

dropout 269, 270

dynamic programming

FrozenLake environment, solving 457

E

early stopping 24, 270

edges 255

Elbow method 331

encoder 446

entropy 120, 121, 122

environment 453

episode 457

epsilon-greedy policy 482

Euclidean distance 316

evidence 52

exploitation 482

exploration 482

exploration phase 26

F

f1 score 66

face image dataset

exploring 98, 99

face images

classifying, with SVMs 98

feature 24

feature-based bagging 141

feature crossing. See  also feature interaction

feature engineering 30, 204, 218, 219, 220, 221, 355

on categorical variables, with Spark 203

feature hashing. See  also hashing trick

feature interaction 207, 209, 210

feature map 382

feature projection 25

features 44, 315

generating 222, 223, 224, 225, 226

feature selection 170

L1 regularization, examining for 170, 171

used, for avoiding overfitting 24

with random forest 180, 181

feedforward neural network 256

fetal state classification

on cardiotocography 104, 105, 106

forget gate 422

FrozenLake

solving, with policy iteration algorithm 464, 465, 466, 467, 468

solving, with value iteration algorithm 460, 461, 462, 463, 464

FrozenLake environment

simulating 457, 458, 459, 460

solving, with dynamic programming 457

fundamental analysis 214

G

Gated Recurrent Unit (GRU) 420

Gaussian kernel 93

generalization 13, 14

Generative Pre-training Transformer (GPT) 448

genetic algorithms (GA) 11

Gensim 285, 294

URL 286

Georgetown-IBM experiment

reference link 283

Gini Impurity 117, 118, 119, 120

Google Cloud Storage

reference link 355

Google Neural Machine Translation (GNMT) 261

gradient boosted trees (GBT) 142, 144, 145

gradient boosting machines 142

gradient descent 158

ad click-through, predicting with logistic regression 165, 166

logistic regression model, training 158, 159, 160, 161, 163, 164

gradients 41

Graphical Processing Units (GPUs) 11

Graphviz

URL 133

GraphX 185

H

Hadoop Distributed File System (HDFS) 192

handwritten digit recognition 46

handwritten digits MNIST dataset

reference link 388

harmonic mean 66

hashing categorical

features 204, 206, 207

hashing collision 205

hashing trick 204

Heterogeneity Activity Recognition Dataset 97

HIGGS Dataset 97

high-order polynomial function 22

high variance 15

holdout method 21

horizontal flipping

for data augmentation 400, 401, 402, 403

hyperplane 76

I

image-based search engines 261

image classification performance

boosting, with PCA 103, 104

ImageDataGenerator module

reference link 400

image recognition 261

IMDb

URL 423

imputing 27

Information Gain 120, 121, 122

inner cross-validation 21

input gate 422

interaction 30

intercept 154

Internet of Things (IoT) 6

interquartile range 29

Iterative Dichotomiser 3 (ID3) 116

K

k

value, selecting 331, 332, 333

Kaggle

URL 8

k equal-sized folds 20

Keras

URL 266

kernel coefficient 93

kernel function 93

kernels

linearly non-separable problems, solving 91, 92, 93, 94, 96

k-fold cross-validation 20

k-means

implementing 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329

implementing, with scikit-learn 329, 330, 331

used, for clustering newsgroups data 316, 333, 334, 335, 336, 337

k-means clustering

working 316, 317

k-nearest neighbors (KNN) 359

L

L1 regularization 169

examining, for feature selection 170, 171

L2 regularization 169

labeled data 315

Labeled Faces in the Wild (LFW) people dataset

reference link 98

label encoding 28

labels 44

Laplace smoothing 54

Lasso 169

latent Dirichlet allocation (LDA)

using, for topic modeling 342, 343, 344, 345

layer 255

layers

adding, to neural network 260

leaf 112

Leaky ReLU 268

learning_curve module

reference link 373

learning rate 158

Leave-One-Out-Cross-Validation (LOOCV) 20

lemmatization 293, 305

liblinear

reference link 80

libsvm

reference link 80

likelihood 52

linear function 268

linear kernel 96

linearly non-separable problems

solving, with kernels 91, 92, 93, 94, 96

linear regression

estimating with 226

example 216

implementing 228, 229, 230, 231, 232

implementing, with scikit-learn 232

implementing, with TensorFlow 233, 234

working 227, 228

LinearSVC

reference link 102

logarithmic loss 158

logic gate

reference link 421

logistic function 152, 153, 256

logistic regression 153, 154, 368

ad click-through, predicting 165, 166

data, classifying 151

implementing, with TensorFlow 178, 180

logistic regression model

testing 201, 203

training 158, 201, 203

training, with gradient descent 158, 159, 160, 161, 163, 164

training, with regularization 169, 170

training, with stochastic gradient descent 166, 168, 169

log loss 158

London FTSE-100

reference link 218

Long Short-Term Memory

long-term dependencies, overcoming 420, 421

Long Short-Term Memory (LSTM) 420

loss function 9

low bias 14

LSTM recurrent cell

forget gate 422

input gate 422

memory unit 422

output gate 422

M

machine 363

machine learning 2

applications 6, 7

core 13

need for 2, 3, 4

prerequisites 7

reinforcement learning 9

supervised learning 9

types 8

unsupervised learning 9

versus automation 5

versus traditional programming 5

machine learning algorithms

development history 11, 12

machine learning library (MLlib) 185

machine learning regression

problems 216

machine learning solution

workflow 348, 349

machine learning tasks 10, 11

machine vision 261

Manhattan distance 316

many-to-many (synced) RNNs 416, 417

many-to-many (unsynced) RNNs 417, 418

many-to-one RNNs 415, 416

margin 78

massive click logs

data, caching 196

data, splitting 195, 196

learning, with Spark 192

loading 192, 193, 194, 195

Massive Open Online Courses (MOOCs) 8

Matplotlib 40

matplotlib package

reference link 299

maximum-margin 79

mean absolute error (MAE) 245

mean squared error (MSE) 18, 154, 227, 258

memory unit 422

Miniconda 37

reference link 37

missing data imputation 351

missing values

dealing with 27

MNIST (Modified National Institute of Standards and Technology) 46

model-free approach 468

models

combining 31

tuning, with cross-validation 70, 72, 73

model training, evaluation, and selection stage

best practices 367, 369, 370, 371, 372, 373, 374

Monte Carlo learning

performing 468

Monte Carlo policy evaluation

performing 470, 472, 473

Moore's law 12

MovieLens

URL 60

movie rating dataset

reference link 60

movie recommender

building, with Naïve Bayes 60, 62, 63, 64, 65

movie review sentiment, analyzing with RNNs 423

data analysis 423, 424, 425, 426

data preprocessing 423, 424, 425, 426

multiple LSTM layers, stacking 429, 430, 431

simple LSTM network, building 426, 428

multiclass classification 46, 47, 268

handling 175, 176, 177

multi-head attention 447

multi-label classification 47, 48

multi-layer perceptron (MLP) 265

multinomial classification 46

multinomial logistic regression 175

multiple classes

dealing with 85, 87, 88, 89, 90, 91

N

Naïve 48

Naïve Bayes 48, 368

implementing 55, 56, 58, 59

implementing, with sci-kit learn 59

mechanics 52, 53, 54, 55

movie recommender, building 60, 62, 63, 64, 65

named entities 285

named entity recognition (NER) 285

NASDAQ Composite

reference link 218

natural language 282

natural language processing (NLP) 261, 282, 283

applications 284, 285

history 283

Natural Language Toolkit (NLTK) 285

negative hyperplane 78

NER 292

nested cross-validation 21

neural machine translation system, Facebook

reference link 283

neural networks 370

building 262

demystifying 254

fine-tuning 273, 274, 275, 276, 277, 278, 279

hidden layer 254, 255

implementing 262, 263, 264, 265

implementing, with scikit-learn 265

implementing, with TensorFlow 266, 267

input layer 254, 255

layers 254, 255

layers, adding 260

output layer 254, 255

overfitting, preventing 269

stock prices, predicting 271

training 271, 273

newsgroups

underlying topics, discovering 337

newsgroups data

clustering, with k-means 316, 333, 334, 335, 336, 337

exploring 298, 299, 300

obtaining 294, 295, 296, 297

visualizing, with t-SNE 307

n-grams 289

NLP libraries

installing 285, 286, 287

nltk

URL 286

NLTK 40

node 112

nodes 255

no free lunch theorem

reference link 8

non-convex function 154

reference link 155

non-exhaustive scheme 20

nonlinear layer 384

non-negative matrix factorization (NMF) 308

used, for topic modeling 338, 339, 340, 341

numerical features 111, 112

categorical features, converting to 148, 150, 151

NumPy 39

URL 38

O

observations 44, 315

one-hot encoding 28, 148

one-hot encoding categorical features 196, 198, 199, 200

one-to-many RNNs 416

online learning

large datasets, training 172, 174, 175

on-policy approach 473

on-policy Monte Carlo control

performing 473, 474, 475, 476, 477

ontology 284

OpenAI

URL 452

OpenAI Gym

installing 452, 453

URL 452

optimal hyperplane

determining 78, 79, 80, 81

ordinal encoding 148, 150

ordinal feature 111

outer cross-validation 21

outliers

handling 82, 83

output gate 422

overfitting 14, 15

avoiding, with cross-validation 19, 20, 21

avoiding, with dimensionality reduction 24

avoiding, with feature selection 24

avoiding, with regularization 22, 24

preventing, in neural networks 269

P

pandas library 39

part-of-speech (PoS) tagging 291, 412

pickle

models, restoring 374, 375

models, saving 374, 375

plot_learning_curve function

reference link 373

policy 456

policy evaluation step 456

policy iteration algorithm

FrozenLake, solving 464, 465, 466, 467, 468

polynomial transformation 30, 361

pooling layer 385, 386

positive hyperplane 78

posterior 52

Power transforms 30

precision 66

predictive variables 44, 315

preprocessing phase 26, 27

principal component analysis (PCA) 308, 358

image classification performance, boosting 103, 104

reference link 103

prior 52

probability 101

reference link 8

Project Gutenberg

URL 432

projection 315

PySpark 40

programming 189, 190, 191, 192

Python 36

setting up 37

Python Imaging Library (PIL) 99

Python packages

installing 38

PyTorch 40

installing 450, 451

references 451

URL 266, 450

Q

Q-learning algorithm

developing 482, 483, 484, 485, 486

Taxi problem, solving 477

qualitative features 111

quantitative features 112

Q-value 473

R

R² 245

radial basis function (RBF) kernel 93

random access memory (RAM) 185

random forest 141, 370

using, for feature selection 180, 181

RBF kernel 96

recall 66

receiver operating characteristic (ROC) 68

receptive fields 384

Rectified Linear Unit (ReLU) 256, 268

recurrent mechanism 413, 414

recurrent neural networks (RNNs) 412

many-to-many (synced) RNNs 416, 417

many-to-many (unsynced) RNNs 417, 418

many-to-one RNNs 415, 416

one-to-many RNNs 416

regression 10, 215

regression algorithms

stock prices, predicting 246, 247, 248, 249, 250

regression forest

implementing 242

regression performance

estimating 244, 245, 246

regression trees 234, 235, 236, 237

regularization

used, for avoiding overfitting 22, 24

used, for training logistic regression model 169, 170

reinforcement learning 9, 453

approaches 456

deterministic 456

policy-based approach 456

stochastic 456

value-based approach 456

reinforcement learning, elements

action 454

agent 454

environment 453

rewards 454

states 454

ReLU function 258

Resilient Distributed Datasets (RDD) 189

reference link 189

returns 455

rewards 453, 454

ridge 169

RNN architecture

learning 412

RNN model

training 418, 419, 420

RNN text generator

building 436, 437, 438

training 438, 439, 440, 441, 444

root 112

root mean squared error (RMSE) 245

rotation

for data augmentation 404

Russell 2000 (RUT) index

reference link 218

S

S3, Amazon Web Services

reference link 355

scaling 29

scikit-learn

decision tree, implementing 133, 134

k-means, implementing with 329, 330, 331

linear regression, implementing 232

Naïve Bayes, implementing 59

neural networks, implementing 265

URL 38

scikit-learn library 40

SciPy 39

Seaborn 40

seaborn package

reference link 299

self-attention 446

semantics 294

semi-supervised learning 10

separating boundary

finding, with SVM 76

separating hyperplane

identifying 77

sequence 412

sequence modeling 412

sequential learning 412

shifting

for data augmentation 405

sigmoid function 152, 256, 268

similarity querying 294

SimpleImputer class

reference link 351

single-layer neural network 254

skip-gram 363

softmax function 268

softmax regression 175

S&P 500 index

reference link 218

spaCy 285, 290

URL 286

Spark

download link 186

fundamentals 184

installing 186, 187

massive click logs, learning with 192

used, for feature engineering on categorical variables 203

Spark, cluster mode approaches

Apache Hadoop YARN 188

Apache Mesos 188

Kubernetes 188

standalone cluster mode 188

Spark, components 184

GraphX 185

MLlib 185

Spark Core 185

Spark SQL 185

Spark Streaming 185

Spark Core 185

Spark, documentation and tutorials

reference link 185

Spark programs

deploying 187

launching 187

Spark SQL 185

Spark Streaming 185

stacking 36

states 453, 454

statistical learning 11

steepest descent 158

stemming 292, 293, 305

step size 158

stochastic gradient descent

used, for training logistic regression model 166, 168, 169

stochastic gradient descent (SGD) 232

stock index 217

stock market 214

stock price data

mining 216, 217

stock prices 214

predicting, with neural networks 271

predicting, with regression algorithms 246, 247, 248, 249, 250

stop words

dropping 304, 305

Storage, in Microsoft Azure

reference link 355

sum of squared errors (SSE) 332

sum of within-cluster distances 332

supervised learning 9

support vector machine (SVM) 48, 242

support vector regression

estimating with 242, 244

support vectors 76, 79

SVM 370

face images, classifying 98

implementing 84, 85

separating boundary, finding 76

SVM-based image classifier

building 100, 101, 102

SVR

implementing 244

T

tanh function 256, 257, 268

targets 315

target variables 315

Taxi environment

reference link 477

simulating 477, 478, 479, 480, 481, 482

Taxi problem

solving, with Q-learning algorithm 477

Tay

reference link 284

t-distributed Stochastic Neighbor Embedding (t-SNE)

for dimensionality reduction 308, 309, 310, 311

newsgroups data, visualizing 307

technical analysis 214

TensorFlow 40

linear regression, implementing 233, 234

logistic regression, implementing 178, 180

models, restoring 376, 377

models, saving 376, 377

neural networks, implementing 266, 267

URL 38

TensorFlow 2 40

term frequency-inverse document frequency (tf-idf) 335, 362

term frequency (tf) 335, 362

terminal node 112

testing samples 13

testing sets 13

TextBlob 285

URL 287

text data, features 301

inflectional and derivational forms of words, reducing 305, 306, 307

occurrence, counting of word token 301, 302, 303, 304

stop words, dropping 304, 305

text preprocessing 304

text datasets, NLTK

reference link 287

text preprocessing 304

tokenization 289, 290

tokens 289

topic 342

topic model 337

topic modeling 294, 338

with latent Dirichlet allocation (LDA) 342, 343, 344, 345

with non-negative matrix factorization (NMF) 338, 339, 340, 341

Torch

URL 450

traditional programming

versus machine learning 5

training samples 13

training sets 13

training sets generation stage

best practices 355, 356, 357, 358, 359, 360, 361, 362, 363, 364

Transformer model 444

architecture 444, 446

transition matrix 460

true positive rate 66

Turing test

reference link 283

U

underfitting 16

unigrams 289

units 255

unlabeled data 9, 315

unsupervised learning 9, 314

association 315

clustering 315

projection 315

types 315

unsupervised learning 308

URL Reputation Dataset 97

V

validation samples 13

validation sets 13

value iteration algorithm 460

FrozenLake, solving 460, 461, 462, 463, 464

vanishing gradient problem 420

variance 17

voting 32

W

War and Peace, writing with RNNs 431

RNN text generator, building 436, 437, 438

RNN text generator, training 438, 439, 440, 442, 443, 444

training data, acquiring 432, 433

training data, analyzing 432, 433

training set, constructing for RNN text generator 433, 435, 436

weak learners 34

weights 153

word embedding 294, 363

with pre-trained models 364, 365, 366, 367

word token

occurrence, counting 301, 302, 303, 304

word_tokenize function 290

word vectorization 294

working environment

setting up 450

X

XGBoost package

reference link 144

XOR gate

reference link 96

Y

Yet Another Resource Negotiator (YARN) 188

YouTube Multiview Video Games Dataset 97