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Applied Machine Learning and AI for Engineers

Name: Applied Machine Learning and AI for Engineers
Author: Jeff Prosise
ISBN: 9781492098058

by Jeff Prosise

November 2022

Intermediate to advanced

425 pages

11h 25m

English

O'Reilly Media, Inc.

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Foreword
Preface
Who Should Read This BookWhy I Wrote This BookRunning the Book’s Code SamplesNavigating This BookConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
I. Machine Learning with Scikit-Learn
1. Machine Learning
What Is Machine Learning?Machine Learning Versus Artificial IntelligenceSupervised Versus Unsupervised LearningUnsupervised Learning with k-Means ClusteringApplying k-Means Clustering to Customer DataSegmenting Customers Using More Than Two DimensionsSupervised Learningk-Nearest NeighborsUsing k-Nearest Neighbors to Classify FlowersSummary
2. Regression Models
Linear RegressionDecision TreesRandom ForestsGradient-Boosting MachinesSupport Vector MachinesAccuracy Measures for Regression ModelsUsing Regression to Predict Taxi FaresSummary
3. Classification Models
Logistic RegressionAccuracy Measures for Classification ModelsCategorical DataBinary ClassificationClassifying Passengers Who Sailed on the TitanicDetecting Credit Card FraudMulticlass ClassificationBuilding a Digit Recognition ModelSummary
4. Text Classification
Preparing Text for ClassificationSentiment AnalysisNaive BayesSpam FilteringRecommender SystemsCosine SimilarityBuilding a Movie Recommendation SystemSummary
5. Support Vector Machines
How Support Vector Machines WorkKernelsKernel TricksHyperparameter TuningData NormalizationPipeliningUsing SVMs for Facial RecognitionSummary
6. Principal Component Analysis
Understanding Principal Component AnalysisFiltering NoiseAnonymizing DataVisualizing High-Dimensional DataAnomaly DetectionUsing PCA to Detect Credit Card FraudUsing PCA to Predict Bearing FailureMultivariate Anomaly DetectionSummary
7. Operationalizing Machine Learning Models
Consuming a Python Model from a Python ClientVersioning Pickle FilesConsuming a Python Model from a C# ClientContainerizing a Machine Learning ModelUsing ONNX to Bridge the Language GapBuilding ML Models in C# with ML.NETSentiment Analysis with ML.NETSaving and Loading ML.NET ModelsAdding Machine Learning Capabilities to ExcelSummary

II. Deep Learning with Keras and TensorFlow
8. Deep Learning
Understanding Neural NetworksTraining Neural NetworksSummary
9. Neural Networks
Building Neural Networks with Keras and TensorFlowSizing a Neural NetworkUsing a Neural Network to Predict Taxi FaresBinary Classification with Neural NetworksMaking PredictionsTraining a Neural Network to Detect Credit Card FraudMulticlass Classification with Neural NetworksTraining a Neural Network to Recognize FacesDropoutSaving and Loading ModelsKeras CallbacksSummary
10. Image Classification with Convolutional Neural Networks
Understanding CNNsUsing Keras and TensorFlow to Build CNNsTraining a CNN to Recognize Arctic WildlifePretrained CNNsUsing ResNet50V2 to Classify ImagesTransfer LearningUsing Transfer Learning to Identify Arctic WildlifeData AugmentationImage Augmentation with ImageDataGeneratorImage Augmentation with Augmentation LayersApplying Image Augmentation to Arctic WildlifeGlobal PoolingAudio Classification with CNNsSummary
11. Face Detection and Recognition
Face DetectionFace Detection with Viola-JonesUsing the OpenCV Implementation of Viola-JonesFace Detection with Convolutional Neural NetworksExtracting Faces from PhotosFacial RecognitionApplying Transfer Learning to Facial RecognitionBoosting Transfer Learning with Task-Specific WeightsArcFacePutting It All Together: Detecting and Recognizing Faces in PhotosHandling Unknown Faces: Closed-Set Versus Open-Set ClassificationSummary
12. Object Detection
R-CNNsMask R-CNNYOLOYOLOv3 and KerasCustom Object DetectionTraining a Custom Object Detection Model with the Custom Vision ServiceUsing the Exported ModelSummary
13. Natural Language Processing
Text PreparationWord EmbeddingsText ClassificationAutomating Text VectorizationUsing TextVectorization in a Sentiment Analysis ModelFactoring Word Order into PredictionsRecurrent Neural Networks (RNNs)Using Pretrained Models to Classify TextNeural Machine TranslationLSTM Encoder-DecodersTransformer Encoder-DecodersBuilding a Transformer-Based NMT ModelUsing Pretrained Models to Translate TextBidirectional Encoder Representations from Transformers (BERT)Building a BERT-Based Question Answering SystemFine-Tuning BERT to Perform Sentiment AnalysisSummary
14. Azure Cognitive Services
Introducing Azure Cognitive ServicesKeys and EndpointsCalling Azure Cognitive Services APIsAzure Cognitive Services ContainersThe Computer Vision ServiceThe Language ServiceThe Translator ServiceThe Speech ServicePutting It All Together: Contoso TravelSummary
Index
About the Author

Content preview from Applied Machine Learning and AI for Engineers

Chapter 7. Operationalizing Machine Learning Models

All of the machine learning models presented so far in this book are written in Python. Models don’t have to be written in Python, but many are, thanks in part to the numerous world-class Python libraries available, including Pandas and Scikit-Learn. ML models written in Python are easily consumed in Python apps. Calling them from other languages such as C++, Java, and C# requires a little more work. You can’t simply call a Python function from C++ as if it were a C++ function. So how do you invoke models written in Python from apps written in other languages? Put another way, how do you operationalize Python models such that they are usable in any app on any platform written in any programming language?

The diagram on the left in Figure 7-1 shows one strategy: wrap the model in a web service and expose its functionality through a REST API. Then any client that can generate an HTTP(S) request can invoke the model. It’s relatively easy to do the wrapping with help from Python frameworks such as Flask. The web service can be hosted locally or in the cloud, and it can be containerized for easy deployment using tools such as Docker.

The diagram on the right shows another strategy—one that’s relatively new but rapidly growing in popularity. It involves exporting ...

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Applied Machine Learning and AI for Engineers

by Jeff Prosise

Chapter 7. Operationalizing Machine Learning Models

Figure 7-1. Architectures for consuming Python models in other languages

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