Index

  • A
  • accessing
    • Amazon S3 using AWS CLI, 199–200
    • AWS, 154–160
    • objects, 191–195
  • Account menu (AWS management console), 158–160
  • AccountOverview intent, customizing, 308–312
  • acl object, 183
  • ACMEBankBotAccountOverview function, 286–299
  • ACMEBankBotTransactionList function, 299–304
  • activity detection, in Amazon Rekognition, 422
  • agent, 11
  • algorithms
    • built-in, 356, 379–384
    • selecting, 23–24
  • Amazon CloudFront, 153–154
  • Amazon Cognito
    • about, 142, 201
    • common tasks, 204–217
    • identity pools, 203, 218–219
    • key concepts, 201–203
    • retrieving client secret, 213–214
    • user pools, 202–213, 218–219
  • Amazon Cognito Federated Identities, 203
  • Amazon Comprehend
    • about, 140–141, 257
    • interactive text analysis using AWS CLI, 262–265
    • key concepts, 257–260
    • text analysis using Management Console, 260–262
    • using with AWS Lambda, 266–274
  • Amazon DynamoDB
    • about, 142, 221
    • adding items to tables, 228–231
    • common tasks, 225–236
    • creating an index, 231–233
    • creating tables, 225–228, 278–285, 433–435
    • as event source for AWS Lambda, 240
    • key concepts, 221–225
    • performing queries in, 235–236
    • performing scans in, 233–235
  • Amazon Glacier, 183
  • Amazon Lex
    • about, 141, 275
    • creating bots, 278–315
    • key concepts, 275–278
  • Amazon Machine Learning
    • about, 141, 317
    • creating datasources, 321–332
    • creating ML models, 337–341
    • creating models with, 319
    • creating real-time prediction endpoints, 346–347
    • key concepts, 317–321
    • making batch predictions, 341–346
    • making predictions using AWS CLI, 347–349
    • using real-time prediction endpoints, 349–350
    • viewing data insights, 332–337
  • Amazon Polly, 141
  • Amazon Rekognition
    • about, 141, 421
    • analyzing images using Management Console, 423–428
    • interactive image analysis with AWS CLI, 428–433
    • key concepts, 421–423
    • using with AWS Lambda, 433–444
  • Amazon S3 (Simple Storage Service)
    • about, 142, 181
    • accessing using AWS CLI, 199–200
    • bucket versioning, 197–199
    • common tasks, 185–200
    • as event source for AWS Lambda, 240
    • key concepts, 181–185
  • Amazon SageMaker
    • about, 141–142, 353
    • creating notebook instances, 357–362
    • deploying DNN classifiers using Google TensorFlow Estimators API and, 408–418
    • key concepts, 353–357
    • notebook instances, 357–362
    • preparing test and training data, 362–364
    • SDK for Python, 354
    • training DNN classifiers using Google TensorFlow Estimators API and, 408–418
    • training models using built-in algorithms on dedicated training instances, 379–384
    • training Scikit-learn models on dedicated training instances, 368–378
    • training Scikit-learn models on notebook instances, 364–368
    • using Google TensorFlow with, 387–418
  • Amazon Transcribe, 141
  • Amazon Translate, 141
  • Amazon Web Services (AWS)
    • about, 135
    • cloud computing, 135–136
    • cloud deployment models, 138–139
    • cloud service models, 136–138
    • ecosystem of, 139–142
    • free-tier accounts, 142–148
    • global infrastructure, 151–160
    • management console, 156–160
  • Anaconda Navigator, 4
  • analyzing
    • images using Amazon Rekognition Management Console, 423–428
    • images with AWS CLI, 428–433
  • ANN (artificial neural network), 388–389
  • API sets, in Amazon Rekognition, 422
  • application virtualization, 136
  • artificial neural network (ANN), 388–389
  • ASR (Automatic Speech Recognition), 141
  • attributes
    • about, 318
    • in Amazon DynamoDB, 222
  • auc() function, 126
  • authentication, 201
  • authorization, 201
  • Autoencoding, 10
  • Automatic Speech Recognition (ASR), 141
  • availability
    • of Amazon Comprehend, 259–260
    • of Amazon Lex, 278
    • of Amazon Machine Learning, 321
    • of Amazon Rekognition, 423
    • of Amazon SageMaker, 357
    • as an advantage of cloud computing, 136
    • of AWS Lambda and, 244
  • availability zones (AZ), 151–153
  • AWS. See Amazon Web Services (AWS)
  • AWS boto3 SDK, 354
  • AWS CLI
    • accessing Amazon S3 using, 199–200
    • Amazon SageMaker and, 354
    • detecting entities with, 263–264
    • detecting key phrases with, 264–265
    • interactive image analysis with, 428–433
    • making predictions using, 347–349
    • sentiment analysis with, 265
    • text analysis with, 262–265
  • AWS DeepLens, 142
  • AWS IAM, 142
  • AWS Lambda
    • about, 142, 237
    • common tasks, 244–254
    • common use cases for, 237–238
    • creating functions, 285–304, 435–444
    • creating Python functions using AWS Management Console, 244–250
    • deleting Python Lambda functions using AWS Management Console, 253–254
    • key concepts, 238–244
    • testing Python functions using AWS Management Console, 250–253
    • using Amazon Comprehend with, 266–274
    • using Amazon Rekognition with, 433–444
  • AWS Management Console
    • access to, 168
    • creating Python Lambda functions using, 244–250
    • deleting Python Lambda functions using, 253–254
    • testing Python Lambda functions using, 250–253
  • AWS public datasets, 30
  • AWS re:Invent, 139
  • AWS Service, 173
  • AWS Signature V4, 417–418
  • axes class, 69
  • axes object, 55–56
  • Axes.pie() method, 70
  • axis labels, 56
  • axis object, 56
  • AZ (availability zones), 151–153
  • B
  • bar charts
    • about, 61–62
    • grouped, 62–65
    • stacked, 65–67
    • stacked percentage, 67–68
  • batch learning, 11–12
  • batch predictions, making, 341–346
  • batch transform, in Amazon SageMaker, 355
  • behavioral biometrics, machine learning for, 7
  • Bezos, Jeff, 139
  • binary classification models, 119–126, 318
  • Black, Benjamin, 139
  • Boston house prices dataset, 29
  • bots
    • about, 275
    • creating in Amazon Lex, 278–315
    • testing, 314–315
  • box plots, 71–73
  • boxplot() function, 39–40, 72
  • BPaaS (business process as a service), 137–138
  • Breast cancer dataset, 30
  • broad network access, as a characteristic of cloud computing, 135
  • bucket versioning, in Amazon S3, 197–199
  • buckets
    • about, 181–182
    • creating, 185–189
  • built-in algorithms
    • Amazon SageMaker, 356
    • training models on dedicated training instances using, 379–384
  • built-in slot types, 277
  • business process as a service (BPaaS), 137–138
  • C
  • changing
    • permissions associated with existing groups, 172–173
    • storage classes of objects, 195–196
  • chatbots. See bots
  • choosing
    • algorithms, 23–24
    • hyperparameter values, 131–132
    • input features, 18–22
  • churn prediction, machine learning for, 7
  • claims, 202
  • classification, 6
  • classification models
    • binary, 119–126
    • evaluating, 119–131
    • multi-class, 126–131
  • client
    • in authentication flow, 202
    • secret, retrieving, 213–214
  • client application, 276
  • cloud computing, 3, 135–136
  • cloud deployment models, 138–139
  • cloud service models, 136–138
  • clustering, 9
  • community cloud, 138
  • computation graph, 387
  • compute requirements, AWS Lambda and, 239
  • confusion_matrix() function, 123, 128
  • consistent reads, in Amazon DynamoDB, 224
  • constant leaf nodes, 388
  • context, AWS Lambda and, 242
  • continuous tasks, reinforcement learning for, 11
  • corr() function, 40
  • costs
    • Amazon S3, 183
    • as an advantage of cloud computing, 136
  • creating
    • AWS Lambda functions, 244–250, 285–304, 435–444
    • batch predictions in Amazon Machine Learning, 341–346
    • bots in Amazon Lex, 278–315
    • buckets, 185–189
    • datasources in Amazon Machine Learning, 321–332
    • identity pools (Amazon Cognito), 214–217
    • indexes in Amazon DynamoDB, 231–233
    • linear regression models with Google TensorFlow, 390–408
    • machine learning models with linear regression, 86–92
    • machine learning models with logistic regression, 101–109
    • machine learning models with Scikit-learn, 79–113
    • ML models in Amazon Machine Learning, 337–341
    • models with Amazon Machine Learning, 319
    • new features, 44–46
    • notebook instances (Amazon SageMaker), 357–362
    • predictions using AWS CLI in Amazon Machine Learning, 347–349
    • real-time prediction endpoints in Amazon Machine Learning, 346–347
    • roles, 173–176
    • tables in Amazon DynamoDB, 225–228, 278–285, 433–435
    • test datasets, 80–86
    • test datasources, 330–332
    • training datasets, 80–86
    • training datasources, 324–330
    • user pools (Amazon Cognito), 204–213
    • users, 167–172
  • credit scoring, machine learning for, 7
  • Cristianini, Nello (author)
    • An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, 96
  • cross-validation, 22
  • custom slot types, 277
  • customizing
    • AccountOverview intent, 308–312
    • ViewTransactionList intent, 312–314
  • cut() function, 44–45
  • D
  • data acquisition, 5
  • data attribute, 27
  • data channels (Amazon SageMaker), 355
  • data formats (Amazon SageMaker), 356
  • data insights, viewing, 332–337
  • data munging, 18
  • data preprocessing techniques
    • about, 31
    • creating new features, 44–46
    • handling missing values, 42–44
    • obtaining data overview, 31–41
    • one-hot encoding categorical features, 47–50
    • transforming numeric features, 46–47
  • Data Transfer Pricing (Amazon S3), 183
  • data visualization
    • about, 51
    • common plots, 58–78
    • Matplotlib, 51–53
    • plot components, 54–58
  • dataframe.plot.bar() function, 66–67
  • datasets
    • about, 27
    • AWS public, 30
    • Kaggle.com, 30
    • Scikit-learn, 27–30
    • UCI machine learning repository, 30–31
  • datasources
    • about, 318
    • Amazon SageMaker, 356
    • creating, 321–332
    • test, 330–332
    • training, 324–330
  • decision trees, 6, 109–113
  • DecisionTreeClassifier class, 110, 131
  • deep learning, 7
  • deleting
    • objects, 196
    • Python AWS Lambda functions using AWS Management Console, 253–254
  • Dense Neural Network (DNN), 390, 408
  • deploy() function, 377, 417
  • deploying DNN classifiers using Google TensorFlow Estimators API and Amazon SageMaker, 408–418
  • DESCR function, 27, 87
  • describe() function, 39
  • descriptive statistics (Amazon Machine Learning), 320–321
  • DetectEntities API, 263–264
  • DetectEntities function, 270
  • detecting
    • entities with AWS CLI, 263–264
    • key phrases with AWS CLI, 264–265
  • DetectKeyPhrases API, 264–265
  • DetectSentiment API, 265
  • developer SDKs, 155–156
  • Diaconis, Persi, 61
  • dimensionality reduction, 10
  • display.max_columns property, 34
  • DNN (Dense Neural Network), 390, 408
  • DNN classifiers, training/deploying using Google TensorFlow Estimators API and Amazon SageMaker, 408–418
  • DNNClassifier class, 412
  • document types, for attributes, 222
  • dominant language (Amazon Comprehend), 259
  • E
  • EBS (Elastic Block Store), 354
  • EC2 (Elastic Compute Block) service, 3, 165, 349, 357–362, 408
  • edge locations, 153–154
  • Elastic Block Store (EBS) service, 354
  • Elastic Compute Block (EC2) service, 165, 349, 357–362, 408
  • Elastic Compute Cloud (EC2) service, 3
  • elasticity, as a characteristic of cloud computing, 136
  • elicit_slot function, 296
  • enterprise identity federation, 163
  • entities
    • Amazon Comprehend, 257–258
    • detecting with AWS CLI, 263–264
  • episodic tasks, reinforcement learning for, 11
  • error function, 6
  • Estimator class, 383
  • estimator_fn function, 411
  • eval_input_fn function, 412, 413–414
  • evaluating
    • classification models, 119–131
    • machine learning models, 115–132
    • model performance, 24–25
    • regression models, 115–119
  • events, AWS Lambda and, 240–242
  • exceptions, AWS Lambda and, 242–243
  • execution environment, AWS Lambda and, 243
  • execution role, AWS Lambda and, 239
  • execution timeout, AWS Lambda and, 239
  • F
  • face collection, in Amazon Rekognition, 422
  • facial recognition, in Amazon Rekognition, 422
  • false negative (FN) count, 121
  • false positive (FP) count, 121
  • feature engineering, 6
  • feature_names attribute, 28
  • features. See input variables
  • Federated Identities (Amazon Cognito), 203
  • figure class, 57
  • figure object, 55
  • fillna() function, 42–43
  • fit() method, 89, 382–383
  • fit_intercept parameter, 90
  • FN (false negative) count, 121
  • FP (false positive) count, 121
  • fraud detection, machine learning for, 7
  • Freedman, David, 61
  • Freedman-Diaconis rule, 61
  • free-tier accounts, signing up for, 142–148
  • functions
    • ACMEBankBotAccountOverview, 286–299
    • ACMEBankBotTransactionList, 299–304
    • auc(), 126
    • AWS Lambda, 238–239, 435–444
    • boxplot(), 39–40, 72
    • confusion_matrix(), 123, 128
    • corr(), 40
    • creating in AWS Lambda, 285–304
    • cut(), 44–45
    • dataframe.plot.bar(), 66–67
    • deploy(), 377, 417
    • DESCR, 27, 87
    • describe(), 39
    • DetectEntities, 270
    • elicit_slot, 296
    • error, 6
    • estimator_fn, 411
    • eval_input_fn, 412, 413–414
    • fillna(), 42–43
    • generate_scatter_plot, 76
    • head(), 34, 35, 80, 393
    • hist(), 37, 59
    • info(), 32, 33
    • isnull(), 33
    • keras_model_fn, 412
    • load_boston(), 29
    • load_breast_cancer(), 30
    • load_diabetes(), 29
    • load_digits(), 29
    • load_iris(), 29
    • load_linnerud(), 29
    • load_wine(), 30
    • make_blobs, 77
    • map(), 413
    • model_fn, 412
    • NumPy s, 53
    • pie(), 69
    • plot.pie(), 69
    • plot.scatter(), 74
    • predict(), 94, 377, 417, 418
    • print(), 94, 106, 118, 119, 126, 129, 418
    • pyplot hist(), 59
    • pyplot pie(), 69
    • qcut(), 45
    • roc_curve(), 124–125
    • scatter_matrix(), 41, 77
    • serving_input_fn, 412
    • set_index(), 35
    • sigmoid, 23–24, 102
    • sklearn.metrics.mean_r2_score(), 119
    • sklearn.metrics.mean_squared_error(), 118
    • softmax, 107
    • sum(), 33
    • suptitle() method/, 57
    • tf.constant(), 388
    • tf.feature_column.numeric_column(), 412
    • tf.get_variable(), 388, 395
    • tf.global_variables_initializer(), 400
    • tf.placeholder(), 388, 394, 395
    • tf.reduce:mean(), 397
    • title(), 58
    • train_input_fn, 412, 413
    • train_test_split(), 80–82
    • validate_customer_identifier(), 298–299
    • value_counts(), 36, 37
    • xlabel(), 56
    • ylabel(), 56
  • G
  • GD (gradient descent) optimization, 397
  • generate_scatter_plot function, 76
  • get_n_splits() parameter, 85
  • Gini scores, 112
  • The Gini Methodology: A Primer on Statistical Methodology (Yitzhaki and Schechtman), 112
  • global infrastructure (AWS)
    • about, 151
    • accessing AWS, 154–160
    • edge locations, 153–154
    • regions and availability zones, 151–153
  • global secondary index, 223
  • global tables, in Amazon DynamoDB, 222
  • Google Authenticator, 178
  • Google TensorFlow
    • about, 387–390
    • creating linear regression models with, 390–408
    • training/deploying DNN classifiers using TensorFlow Estimators API and Amazon SageMaker, 408–418
    • using Amazon SageMaker with, 387–418
  • gradient descent (GD) optimization, 397
  • Graphviz, 111
  • grid() method, 57
  • grids, in plots, 57
  • GridSearchCV class, 131
  • grouped bar charts, 62–65
  • groups
    • about, 163–164
    • modifying permissions associated with existing, 172–173
  • H
  • handler name, AWS Lambda and, 239
  • handlers, AWS Lambda and, 239
  • handling missing values, 42–44
  • Handwritten digits dataset, 29
  • hardware MFA device, 178
  • hardware virtualization, 136
  • HCA (Hierarchical Cluster Analysis), 9
  • head() function, 34, 35, 80, 393
  • hidden layer, 389
  • Hierarchical Cluster Analysis (HCA), 9
  • hist() function, 37, 59
  • histograms, 58–62
  • Home menu (AWS management console), 157
  • Human Genome Project, 30
  • hybrid cloud, 139
  • hyperparameter values, selecting, 131–132
  • I
  • IaaS (infrastructure as a service), 136, 137
  • IAM Policy Language, 169
  • Identity and Access Management (IAM)
    • about, 161
    • common tasks, 165–180
    • key concepts, 161–165
  • identity federation, 162–163
  • identity pool (Amazon Cognito)
    • about, 203
    • compared with user pool, 218–219
    • creating, 214–217
  • identity provider, 202
  • images
    • analyzing using Amazon Rekognition Management Console, 423–428
    • analyzing with AWS CLI, 428–433
  • incremental learning, 12
  • index, creating in Amazon DynamoDB, 231–233
  • info() function, 32, 33
  • infrastructure as a service (IaaS), 136, 137
  • input data, 318
  • input features, selecting, 18–22
  • input layer, 389
  • input variables, 5
    • creating new, 44–46
    • one-hot encoding categorical, 47–50
  • instance types, 355
  • instance-based learning, 12
  • insurance premium calculation, machine learning for, 7
  • intent (Amazon Lex), 276
  • An Introduction to Support Vector Machines and Other Kernel-based Learning Methods (Cristianini and Shawe-Taylor), 96
  • IPython Notebook, 4
  • Iris plants dataset, 29
  • isnull() function, 33
  • items
    • adding to tables in Amazon DynamoDB, 228–231
    • in Amazon DynamoDB, 222
  • J
  • Jason Web Token (JWT)-based identity token, 202
  • Jenkins, 238
  • Jupyter Notebook, 4
  • K
  • Kaggle.com datasets, 30
  • keras_model_fn function, 412
  • key phrases
    • Amazon Comprehend, 258
    • detecting with AWS CLI, 264–265
  • k-fold cross validation, 84–86
  • KMeans class, 382–383
  • k-means clustering, 9
  • L
  • L1/L2 regularization, 319
  • labeled data, 17
  • lambda_handler() method, 295
  • language support, Amazon Comprehend and, 259
  • language-specific SDK, 354
  • Latent Dirichlet Allocation (LDA)-based model, 10, 259
  • LDA (Linear Discriminant Analysis), 10
  • leaf nodes, 388
  • Linear Discriminant Analysis (LDA), 10
  • linear regression
    • about, 23
    • creating machine learning models with, 86–92
    • creating models with Google TensorFlow, 390–408
    • defined, 6
  • LinearRegression class, 87
  • Linnerud dataset, 29
  • load_boston() function, 29
  • load_breast_cancer() function, 30
  • load_diabetes() function, 29
  • load_digits() function, 29
  • load_iris() function, 29
  • load_linnerud() function, 29
  • load_wine() function, 30
  • local secondary index, 223
  • location, 318
  • logging, AWS Lambda and, 242
  • logistic regression
    • about, 23
    • creating machine learning models with, 101–109
    • defined, 6
  • M
  • machine learning
    • about, 3–4
    • real-world applications of, 7
    • terminology for, 5–7
    • tools for, 4–5
    • traditional approach versus, 13–25
    • types of systems for, 8–12
  • machine learning application services, 140–141
  • machine learning as a service (MLaaS), 138
  • machine learning datasets. See datasets
  • machine learning models
    • defined, 5
    • evaluating, 115–132
  • machine learning platform services, 141–142
  • maintenance, AWS Lambda and, 238
  • make_blobs function, 77
  • management console (AWS), 156–160
  • map() function, 413
  • MATLAB, 51
  • Matplotlib, 5, 51–53
  • Maven, 238
  • maximum model size parameter, 319–320
  • maximum number of training passes parameter, 320
  • McCulloch, Warren, 388
  • measured service, as a characteristic of cloud computing, 135
  • MFA (multifactor authentication), securing root account with, 176–179
  • MinMaxScaler class, 46
  • missing values, handling, 42–44
  • ML models
    • about, 318–319
    • creating, 337–341
  • MLaaS (machine learning as a service), 138
  • model-based learning, 12
  • model_fn function, 412
  • models
    • building with Amazon Machine Learning, 319
    • evaluating performance of, 24–25
    • versioning in Amazon Rekognition, 423
  • modifying
    • permissions associated with existing groups, 172–173
    • storage classes of objects, 195–196
  • multi-class classification models, 126–131, 318
  • multifactor authentication (MFA), securing root account with, 176–179
  • munge, 18
  • music recommendations, machine learning for, 7
  • N
  • Natural Language Processing (NLP), 140–141, 257–259
  • Natural Language Understanding (NLU), 141
  • neural networks, 6–7
  • neurons, 389
  • NLP (Natural Language Processing), 140–141, 257–259
  • NLU (Natural Language Understanding), 141
  • non-storage operations, in Amazon Rekognition, 423
  • notebook instances (Amazon SageMaker)
    • creating, 357–362
    • training Scikit-learn models on, 364–368
  • n_splits parameter, 85
  • numeric features, transforming, 46–47
  • NumPy, 5
  • NumPy functions, 53
  • O
  • OAuth 2.0, 202
  • object key, 182
  • object location, in Amazon Rekognition, 422
  • object metadata (Amazon S3), 184–185
  • object value, 182
  • objects
    • accessing, 191–195
    • changing storage classes of, 195–196
    • deleting, 196
    • detecting in Amazon Rekognition, 421
  • observation, 318
  • obtaining data, 31–41
  • On Demand Mode, 224
  • on-demand self-service, as a characteristic of cloud computing, 135
  • one-hot encoding categorical features, 47–50
  • one-versus-one (OVO) approach, 104–105
  • one-versus-rest (OVR) approach, 104
  • OneVsOneClassifier class, 105
  • OneVsRestClassifier class, 105
  • Onset of diabetes dataset, 29
  • OpenID Connect, 202
  • operation nodes (op nodes), 388
  • output layer, 389
  • OVO (one-versus-one) approach, 104–105
  • OVR (one-versus-rest) approach, 104
  • P
  • PaaS (platform as a service), 137
  • Pandas, 5
  • partition key, 222–223
  • partition key and sort key, 223
  • password rotation policy, setting up for IAM, 179–180
  • PCA (Principal Component Analysis), 10
  • performing
    • queries in Amazon DynamoDB, 235–236
    • scans in Amazon DynamoDB, 233–235
  • permissions, modifying, 172–173
  • pie charts, 69–71
  • pie() function, 69
  • Pillow, 5
  • Pinkham, Chris, 139
  • Pitts, Walter, 388
  • placeholder leaf nodes, 388
  • platform as a service (PaaS), 137
  • plot.pie() function, 69
  • plots
    • common, 58–78
    • components of, 54–58
  • plot.scatter() function, 74
  • plt.bar() statement, 67
  • policy, 164
  • predict() function, 94, 377, 417, 418
  • prediction endpoints (Amazon SageMaker), 355
  • prediction instances (Amazon SageMaker), 355
  • predictions, making using AWS CLI, 347–349
  • predict_proba() method, 103, 106, 107
  • preparing
    • test data in Amazon SageMaker, 362–364
    • test sets, 22
    • training data in Amazon SageMaker, 362–364
    • training sets, 22
  • pricing
    • Amazon Comprehend and, 259–260
    • Amazon Lex, 278
    • Amazon Machine Learning, 321
    • of Amazon Rekognition, 423
    • Amazon SageMaker, 357
    • AWS Lambda and, 244
  • primary keys (Amazon DynamoDB), 222–223
  • Principal Component Analysis (PCA), 10
  • print() function, 94, 106, 118, 119, 126, 129, 418
  • print() statement, 59–60
  • private cloud, 138
  • product recommendations, machine learning for, 7
  • programmatic access, 168
  • programming mode (Amazon SageMaker), 354
  • programming model
    • Amazon Lex, 277–278
    • AWS Lambda and, 239–243
  • Provisioned Mode, 225
  • PubChem, 30
  • public cloud, 138
  • pyplot hist() function, 59
  • pyplot module, 51–53
  • pyplot pie() function, 69
  • Python
    • about, 4
    • creating Lambda functions using AWS Management Console, 244–250
    • deleting Lambda functions using AWS Management Console, 253–254
    • testing Lambda functions using AWS Management Console, 250–253
  • Python, data visualization in
    • about, 51
    • common plots, 58–78
    • Matplotlib, 51–53
    • plot components, 54–58
  • PyTorch, 5
  • Q
  • qcut() function, 45
  • queries (Amazon DynamoDB), 223, 235–236
  • R
  • R language, 4
  • R2 metric, 119
  • raise statement, 242–243
  • random_state parameter, 80, 85
  • read consistency (Amazon DynamoDB), 224
  • read/write capacity modes (Amazon DynamoDB), 224–225
  • real-time prediction endpoints
    • creating, 346–347
    • using, 349–350
  • receiver operating characteristics (ROC), 124
  • Reduced Redundancy Storage (RRS), 182
  • regions (AWS), 151–153
  • Regions menu (AWS management console), 160
  • regression
    • defined, 6
    • machine learning models used for, 318
  • regression models, evaluating, 115–119
  • regularization, 319
  • regularization amount parameter, 320
  • regularization type parameter, 320
  • reinforcement learning, 11
  • requests, Amazon S3 costs for, 183
  • Resource Groups menu (AWS management console), 157–158
  • resource pooling, as a characteristic of cloud computing, 135
  • resource-based policy, 164
  • REST APIs, 218–219
  • RESTful web services, 155
  • retrieving Amazon Cognito client secret, 213–214
  • RMSE (root mean squared error), 87, 117–119
  • ROC (receiver operating characteristics), 124
  • roc_curve() function, 124–125
  • roles
    • about, 164–165
    • creating, 173–176
  • root account
    • about, 161
    • securing with MFA, 176–179
  • root mean squared error (RMSE), 87, 117–119
  • RRS (Reduced Redundancy Storage), 182
  • run() method, 400
  • S
  • SaaS (software as a service), 137
  • SAML 2.0 Federation, 174
  • Samuel, Arthur (scientist), 4
  • scalar types, for attributes, 222
  • scans (Amazon DynamoDB), 223–224, 233–235
  • scatter plots, 73–78
  • scatter_matrix() function, 41, 77
  • scene detection, in Amazon Rekognition, 422
  • Schechtman, Edna (author)
    • The Gini Methodology: A Primer on Statistical Methodology, 112
  • schema, 318
  • Scikit-learn
    • about, 4, 79–80
    • creating machine learning models with, 79–113
    • creating training and test datasets, 80–86
    • datasets for, 27–30
    • decision trees, 109–113
    • k-fold cross validation, 84–86
    • linear regression, 86–92
    • logistic regression, 101–109
    • support vector machines, 92–101
    • training models on Amazon SageMaker notebook instances, 364–368
    • training models on dedicated training instances, 368–378
  • Seaborn, 52
  • secondary indexes (Amazon DynamoDB), 223
  • securing root account with MFA, 176–179
  • selecting
    • algorithms, 23–24
    • hyperparameter values, 131–132
    • input features, 18–22
  • semi-supervised learning, 10–11
  • sentiment (Amazon Comprehend), 258
  • sentiment analysis, with AWS CLI, 265
  • serverless back end, AWS Lambda and, 237
  • service limitations, AWS Lambda and, 244
  • Services menu (AWS management console), 157
  • serving_input_fn function, 412
  • set types, for attributes, 222
  • set_index() function, 35
  • set_title() method, 58
  • set_xlabel() method, 56
  • set_ylabel() method, 56
  • Shawe-Taylor, John (author)
    • An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, 96
  • shuffle parameter, 85
  • shuffle type parameter, 320
  • sigmoid function, 23–24, 102
  • sklearn.metrics.mean_r2_score() function, 119
  • sklearn.metrics.mean_squared_error() function, 118
  • slot (Amazon Lex), 276–277
  • SM_CHANNEL_TRAINING variable, 371
  • SM_MODEL_DIR variable, 371
  • SM_OUTPUT_DATADIR variable, 371
  • softmax function, 107
  • software as a service (SaaS), 137
  • split() parameter, 85
  • stacked bar charts, 65–67
  • stacked percentage bar charts, 67–68
  • Standard - IA storage class, 182
  • Standard storage class, 182
  • StandardScaler class, 46
  • stochastic gradient descent, 402
  • storage
    • Amazon S3 costs for, 183
    • changing for objects, 195–196
  • storage class, 182–183
  • Storage Management Pricing (Amazon S3), 183
  • storage-based operations, in Amazon Rekognition, 423
  • stratify parameter, 82
  • streams, AWS Lambda and, 238
  • subresources (Amazon S3), 183
  • sum() function, 33
  • supervised learning, 8–9
  • Support menu (AWS management console), 160
  • support services, 142
  • support vector machine (SVM), 92–101
  • supported languages, for AWS Lambda, 238
  • suptitle() method/function, 57
  • SVM (support vector machine), 92–101
  • syntax (Amazon Comprehend), 258
  • system-defined metadata (Amazon S3), 184
  • T
  • tables
    • adding items to in Amazon DynamoDB, 228–231
    • Amazon DynamoDB, 433–435
    • in Amazon DynamoDB, 222
    • creating in Amazon DynamoDB, 225–228, 278–285, 433–435
  • target attribute, 28
  • target attributes, 318
  • target variable, 6
  • target_names attribute, 28
  • tasks (IAM), 165–180
  • TensorFlow, 5
  • TensorFlow class, 415
  • tensors, 387
  • terminology, 5–7
  • test data
    • defined, 6
    • preparing in Amazon SageMaker, 362–364
  • test datasets, creating, 80–86
  • test datasources, creating, 330–332
  • test sets, preparing, 22
  • testing
    • bots, 314–315
    • Python AWS Lambda functions using AWS Management Console, 250–253
  • test_size parameter, 80
  • text analysis
    • with AWS CLI, 262–265
    • using Amazon Comprehend Management Console, 260–262
  • tf.constant() function, 388
  • tf.feature_column.numeric_column() function, 412
  • tf.get_variable() function, 388, 395
  • tf.global_variables_initializer() function, 400
  • tf.GradientDescentOptimizer class, 398
  • tf.matmul() operator, 396
  • tf.placeholder() function, 388, 394, 395
  • tf.reduce:mean() function, 397
  • tf.reset_default_graph() statement, 395
  • tf.square() operator, 397
  • tf.zeros() operator, 395
  • Titanic dataset, 31–50, 321–332
  • title, of plots, 57–58
  • title() function, 58
  • TN (true negative) count, 121
  • tools, 4–5
  • topic modeling, Amazon Comprehend and, 259
  • torrent object, 183
  • TP (true positive) count, 121
  • traditional approach, machine learning versus, 13–25
  • training
    • datasources, 324–330
    • defined, 5
    • DNN classifiers using Google TensorFlow Estimators API and Amazon SageMaker, 408–418
    • models using built-in algorithms on dedicated training instances in Amazon SageMaker, 379–384
    • Scikit-learn models on dedicated training instances in Amazon SageMaker, 368–378
    • Scikit-learn models on notebook instances in Amazon SageMaker, 364–368
  • training data
    • defined, 6
    • preparing in Amazon SageMaker, 362–364
  • training datasets, creating, 80–86
  • training datasources, creating, 324–330
  • training instances
    • training models using built-in algorithms on dedicated, 379–384
    • training Scikit-learn models on dedicated, 368–378
  • training jobs (Amazon SageMaker), 354–355
  • training parameters (Amazon Machine Learning), 319–320
  • training sets, preparing, 22
  • train_input_fn function, 412, 413
  • train_test_split() function, 80–82
  • Transfer Acceleration, Amazon S3 costs for, 183
  • transforming numeric features, 46–47
  • triggers
    • Amazon Cognito, 212–213
    • AWS Lambda, 237
  • true negative (TN) count, 121
  • true positive (TP) count, 121
  • Tukey, John, 71
  • U
  • U2F Key, 177
  • UCI machine learning repository, 30–31
  • unsupervised learning, 9–10
  • user pool (Amazon Cognito)
    • about, 202–203
    • compared with identity pool, 218–219
    • creating, 204–213
  • user-based policy, 164
  • user-defined metadata, 185
  • users
  • utterance (Amazon Lex), 277
  • V
  • validate_customer_identifier() function, 298–299
  • value_counts() function, 36, 37
  • variable leaf nodes, 388
  • version ID, 182
  • video recommendations, machine learning for, 7
  • viewing data insights, 332–337
  • ViewTransactionList intent, customizing, 312–314
  • virtual MFA device, 177
  • W
  • Web Identity, 174
  • web identity federation, 163
  • websites
    • Amazon Comprehend, 259, 260, 270
    • Amazon DynamoDB, 221, 225
    • Amazon Lex, 278
    • Amazon Machine Learning, 321, 336
    • Amazon Machine Learning and AWS CLI, 347
    • Amazon Polly, 141
    • Amazon Rekognition, 423, 439
    • Amazon SageMaker pricing/availability, 357
    • Amazon Transcribe, 141
    • Amazon Translate, 141
    • Anaconda Navigator, 4
    • attribute data types, 222
    • availability zones (AZ), 152–153
    • AWS APIs, 262
    • AWS DeepLens, 142
    • AWS Lambda pricing, 244
    • AWS services, 240, 317
    • AWS Signature V4, 417–418
    • batch predictions with Amazon SageMaker, 355
    • batch transforms, 376, 416
    • building custom Estimators, 411
    • built-in algorithms, 356
    • chatbots and third-party messaging platforms, 314
    • creating feature columns, 412
    • DNNClassifier class, 412
    • Docker registries for Scikit-learn Docker images, 368
    • Docker registries for TensorFlow Docker images, 408
    • environment variables, 371
    • Estimator class, 383
    • gradient descent technique, 397
    • IAM Policy Language, 169
    • IDEs and tools for creating Lambda code, 238
    • Jupyter Notebook, 4
    • Kaggle.com, 30–31
    • KMeans class, 382
    • language detection APIs, 259
    • Matplotlib, 5
    • monitoring Amazon Rekognition, 427
    • NumPy, 5
    • OpenID Connect specifications, 207
    • Pandas, 5
    • Pillow, 5
    • prediction instance types, 355
    • PyTorch, 5
    • real-time prediction API, 350
    • regions (AWS), 152–153
    • sample event data, 242
    • Scikit-learn, 4
    • serving models with TensorFlow Serving, 414
    • SKLearn class parameters, 374
    • standard libraries with containers, 243
    • TensorFlow, 5
    • TensorFlow APIs, 390
    • TensorFlow class, 415
    • tf.GradientDescentOptimizer class, 398
    • tf.matmul() operator, 396
    • tf.placeholder() function, 395
    • tf.zeros() operator, 395
    • Titanic dataset, 321
    • Titanic Machine Learning From Disaster, 321
    • training instance types, 355
  • Wikipedia, 30
  • Wine recognition dataset, 30
  • with-as statement, 400
  • X
  • xlabel() function, 56
  • Y
  • Yitzhaki, Shlomo (author)
    • The Gini Methodology: A Primer on Statistical Methodology, 112
  • ylabel() function, 56
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