book

Machine Learning with BigQuery ML

Name: Machine Learning with BigQuery ML
Author: Alessandro Marrandino
ISBN: 9781800560307

by Alessandro Marrandino

June 2021

Intermediate to advanced

344 pages

6h 44m

English

Packt Publishing

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Machine Learning with BigQuery ML
ContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesCode in Action Download the color imagesConventions usedGet in touchReviews
Section 1: Introduction and Environment Setup
Chapter 1: Introduction to Google Cloud and BigQuery
Introducing Google Cloud PlatformInteracting with GCPDiscovering GCP's key differentiatorsExploring AI and ML services on GCPCore platform servicesBuilding blocksSolutionsIntroducing BigQueryBigQuery architectureBigQuery's advantages over traditional data warehousesInteracting with BigQueryBigQuery data structuresDiscovering BigQuery MLBigQuery ML benefitsBigQuery ML algorithmsUnderstanding BigQuery pricingBigQuery pricingBigQuery ML pricingFree operations and free tiersPricing calculatorSummaryFurther resources
Chapter 2: Setting Up Your GCP and BigQuery Environment
Technical requirementsCreating your GCP account and projectRegistering a GCP accountExploring Google Cloud ConsoleCreating a GCP projectActivating BigQueryDiscovering the BigQuery web UIExploring the BigQuery public datasetsSearching for a public datasetAnalyzing a tableSummaryFurther reading
Chapter 3: Introducing BigQuery Syntax
Technical requirementsCreating a BigQuery datasetDiscovering BigQuery SQLCRUD operationsDiving into BigQuery MLSummaryFurther resources
Section 2: Deep Learning Networks
Chapter 4: Predicting Numerical Values with Linear Regression
Technical requirementsIntroducing the business scenarioDiscovering linear regressionExploring and understanding the datasetUnderstanding the dataChecking the data's qualitySegmenting the datasetTraining the linear regression modelEvaluating the linear regression modelUtilizing the linear regression modelDrawing business conclusionsSummaryFurther reading
Chapter 5: Predicting Boolean Values Using Binary Logistic Regression
Technical requirementsIntroducing the business scenarioDiscovering binary logistic regressionExploring and understanding the datasetUnderstanding the dataSegmenting the datasetTraining the binary logistic regression modelEvaluating the binary logistic regression modelUsing the binary logistic regression modelDrawing business conclusionsSummaryFurther resources
Chapter 6: Classifying Trees with Multiclass Logistic Regression
Technical requirementsIntroducing the business scenarioDiscovering multiclass logistic regressionExploring and understanding the datasetUnderstanding the dataChecking the data qualitySegmenting the datasetTraining the multiclass logistic regression modelEvaluating the multiclass logistic regression modelUsing the multiclass logistic regression modelDrawing business conclusionsSummaryFurther resources

Section 3: Advanced Models with BigQuery ML
Chapter 7: Clustering Using the K-Means Algorithm
Technical requirementsIntroducing the business scenarioDiscovering K-Means clusteringExploring and understanding the datasetUnderstanding the dataChecking the data qualityCreating the training datasetsTraining the K-Means clustering modelEvaluating the K-Means clustering modelUsing the K-Means clustering modelDrawing business conclusionsSummaryFurther resources
Chapter 8: Forecasting Using Time Series
Technical requirementsIntroducing the business scenarioDiscovering time series forecastingExploring and understanding the datasetUnderstanding the dataChecking the data qualityCreating the training datasetTraining the time series forecasting modelEvaluating the time series forecasting modelUsing the time series forecasting modelPresenting the forecastSummaryFurther resources
Chapter 9: Suggesting the Right Product by Using Matrix Factorization
Technical requirementsIntroducing the business scenarioDiscovering matrix factorizationConfiguring BigQuery Flex SlotsExploring and preparing the datasetUnderstanding the dataCreating the training datasetTraining the matrix factorization modelEvaluating the matrix factorization modelUsing the matrix factorization modelDrawing business conclusionsSummaryFurther resources
Chapter 10: Predicting Boolean Values Using XGBoost
Technical requirementsIntroducing the business scenarioDiscovering the XGBoost Boosted Tree classification modelExploring and understanding the datasetChecking the data qualitySegmenting the datasetTraining the XGBoost classification modelEvaluating the XGBoost classification modelUsing the XGBoost classification modelDrawing business conclusionsSummaryFurther resources
Chapter 11: Implementing Deep Neural Networks
Technical requirementsIntroducing the business scenarioDiscovering DNNsDNNs in BigQuery MLPreparing the datasetTraining the DNN modelsEvaluating the DNN modelsUsing the DNN modelsDrawing business conclusionsDeep neural networks versus linear modelsSummaryFurther resources
Section 4: Further Extending Your ML Capabilities with GCP
Chapter 12: Using BigQuery ML with AI Notebooks
Technical requirementsDiscovering AI Platform NotebooksAI Platform Notebooks pricingConfiguring the first notebookImplementing BigQuery ML models within notebooksCompiling the AI notebookRunning the code in the AI notebookSummaryFurther resources
Chapter 13: Running TensorFlow Models with BigQuery ML
Technical requirementsIntroducing TensorFlowDiscovering the relationship between BigQuery ML and TensorFlowUnderstanding commonalities and differencesCollaborating with BigQuery ML and TensorFlowConverting BigQuery ML models into TensorFlowTraining the BigQuery ML to export itExporting the BigQuery ML modelRunning TensorFlow models with BigQuery MLSummaryFurther resources
Chapter 14: BigQuery ML Tips and Best Practices
Choosing the right BigQuery ML algorithmPreparing the datasetsWorking with high-quality dataSegmenting the datasetsUnderstanding feature engineeringTuning hyperparametersUsing BigQuery ML for online predictionsSummaryFurther resources
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Content preview from Machine Learning with BigQuery ML

Chapter 11: Implementing Deep Neural Networks

Deep Neural Networks (DNNs) are one of the most advanced techniques to implement machine learning algorithms. They're widely used for different use cases and can be considered pervasive in everyday life.

When we interact with a virtual assistant, or we use mobile applications for automatic translation and image recognition, we're leveraging the capabilities of DNNs trained with large datasets of audio and images.

After reading this chapter, you'll be able to develop, evaluate, and test a DNN using BigQuery ML. In this chapter, we'll see all the stages necessary to implement a DNN by using BigQuery ML to predict the duration of rentals related to the New York City bike-sharing service.

Using BigQuery ...

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ISBN: 9781800560307

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Machine Learning with BigQuery ML

by Alessandro Marrandino

Chapter 11: Implementing Deep Neural Networks

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.