book

Practical Automated Machine Learning Using H2O.ai

Name: Practical Automated Machine Learning Using H2O.ai
Author: Salil Ajgaonkar
ISBN: 9781801074520

by Salil Ajgaonkar

September 2022

Beginner to intermediate

396 pages

9h 36m

English

Packt Publishing

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Practical Automated Machine Learning Using H2O.ai Copyright © 2022 Packt Publishing
ContributorsAbout the authorAbout the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code files Download the color imagesConventions usedGet in touchShare Your Thoughts
Part 1 H2O AutoML Basics
Chapter 1: Understanding H2O AutoML Basics
Technical requirementsUnderstanding AutoML and H2O AutoMLAutoMLH2O AutoMLMinimum system requirements to use H2O AutoMLInstalling JavaBasic implementation of H2O using PythonInstalling PythonInstalling H2O using PythonBasic implementation of H2O using RInstalling RInstalling H2O using RTraining your first ML model using H2O AutoMLUnderstanding the Iris flower datasetModel trainingSummary
Chapter 2: Working with H2O Flow (H2O’s Web UI)
Technical requirementsUnderstanding the basics of H2O FlowDownloading and launching H2O FlowExploring H2O FlowWorking with data functions in H2O FlowImporting the datasetParsing the datasetObserving the dataframeSplitting a dataframeWorking with model training functions in H2O FlowUnderstanding the AutoML parameters in H2O FlowTraining and understanding models using AutoML in H2O FlowWorking with prediction functions in H2O FlowMaking predictions using H2O FlowUnderstanding the prediction resultsSummary
Part 2 H2O AutoML Deep Dive
Chapter 3: Understanding Data Processing
Technical requirementsReframing your dataframeCombining columns from two dataframesCombining rows from two dataframesMerging two dataframesHandling missing values in the dataframeFilling NA valuesReplacing values in a frameImputationManipulating feature columns of the dataframeSorting columnsChanging column typesTokenization of textual dataEncoding data using target encodingSummary
Chapter 4: Understanding H2O AutoML Architecture and Training
Observing the high-level architecture of H2OObserving the client layerObserving the JVM component layerLearning about the flow of interaction between the client and the H2O serviceLearning about H2O client-server interactions during the ingestion of dataKnowing the sequence of interactions in H2O during model trainingUnderstanding how H2O AutoML performs hyperparameter optimization and trainingUnderstanding hyperparametersUnderstanding hyperparameter optimization Summary
Chapter 5: Understanding AutoML Algorithms
Understanding the different types of ML algorithmsUnderstanding the Generalized Linear Model algorithmIntroduction to linear regressionUnderstanding the assumptions of linear regressionWorking with a Generalized Linear ModelUnderstanding the Distributed Random Forest algorithmIntroduction to decision treesIntroduction to Random ForestUnderstanding Extremely Randomized TreesUnderstanding the Gradient Boosting Machine algorithmBuilding a Gradient Boosting MachineUnderstanding what is Deep Learning Understanding neural networksSummary
Chapter 6: Understanding H2O AutoML Leaderboard and Other Performance Metrics
Exploring the H2O AutoML leaderboard performance metricsUnderstanding the mean squared error and the root mean squared errorWorking with the confusion matrixCalculating the receiver operating characteristic and its area under the curve (ROC-AUC)Calculating the precision-recall curve and its area under the curve (AUC-PR)Working with log lossExploring other model performance metricsUnderstanding the F1 score performance metricCalculating the absolute Matthews correlation coefficientMeasuring the R2 performance metricSummary

Chapter 7: Working with Model Explainability
Technical requirementsWorking with the model explainability interfaceImplementing the model explainability interface in PythonImplementing the model explainability interface in RExploring the various explainability featuresUnderstanding residual analysisUnderstanding variable importanceUnderstanding feature importance heatmapsUnderstanding model correlation heatmapsUnderstanding partial dependency plotsUnderstanding SHAP summary plotsUnderstanding individual conditional expectation plotsUnderstanding learning curve plotsSummary
Part 3 H2O AutoML Advanced Implementation and Productization
Chapter 8: Exploring Optional Parameters for H2O AutoML
Technical requirementsExperimenting with parameters that support imbalanced classesUnderstanding undersampling the majority classUnderstanding oversampling the minority classWorking with class balancing parameters in H2O AutoMLExperimenting with parameters that support early stoppingUnderstanding early stoppingWorking with early stopping parameters in H2O AutoMLExperimenting with parameters that support cross-validationUnderstanding cross-validationWorking with cross-validation parameters in H2O AutoMLSummary
9
Exploring Miscellaneous Features in H2O AutoMLTechnical requirementsUnderstanding H2O AutoML integration in scikit-learnBuilding and installing scikit-learnExperimenting with scikit-learnUsing H2O AutoML in scikit-learnUnderstanding H2O AutoML event loggingSummary
Chapter 10: Working with Plain Old Java Objects (POJOs)
Technical requirementsIntroduction to POJOsExtracting H2O models as POJOsDownloading H2O models as POJOs in PythonDownloading H2O models as POJOs in RDownloading H2O models as POJOs in H2O FlowUsing a H2O model as a POJOSummary
Chapter 11: Working with Model Object, Optimized (MOJO)
Technical requirementsUnderstanding what a MOJO isExtracting H2O models as MOJOsExtracting H2O models as MOJOs in PythonExtracting H2O models as MOJOs in RExtracting H2O models as MOJOs in H2O FlowViewing model MOJOsUsing H2O AutoML model MOJOs to make predictionsSummary
Chapter 12: Working with H2O AutoML and Apache Spark
Technical requirementsExploring Apache SparkUnderstanding the components of Apache SparkUnderstanding the Apache Spark architectureUnderstanding what a Resilient Distributed Dataset isExploring H2O Sparkling WaterDownloading and installing H2O Sparkling WaterImplementing Spark and H2O AutoML using H2O Sparkling WaterSummary
Chapter 13: Using H2O AutoML with Other Technologies
Technical requirementsUsing H2O AutoML and Spring BootUnderstanding the problem statementDesigning the architecture Working on the implementationUsing H2O AutoML and Apache StormWhat is Apache Storm?Understanding the problem statementDesigning the architecture Working on the implementationSummary
Index
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Overview

Dive into the world of automated machine learning with H2O.ai's powerful tools. This book takes you through the essential ins and outs of using H2O AutoML for training, optimizing, and deploying machine learning models seamlessly. Designed with practical knowledge in mind, it provides the know-how to integrate advanced ML capabilities into your projects effortlessly.

What this Book will help me do

Master the fundamentals of H2O AutoML and streamline your machine learning workflows.
Gain expertise in using the H2O Flow Web UI for enhanced user experience and control.
Learn to leverage H2O Explainability to understand and improve model performance.
Acquire skills to integrate H2O with technologies like Apache Spark, Spring Framework, and more.
Understand how to use POJO and MOJO for deploying machine learning models in production environments.

Author(s)

Salil Ajgaonkar is an accomplished computer scientist specializing in machine learning and automation technologies. With years of professional expertise in implementing real-world machine learning solutions, Salil leverages his in-depth understanding to craft resources that are approachable for developers at any skill level. His focus on practical applications ensures readers gain hands-on experience.

Who is it for?

This book is tailored for software developers, aspiring data scientists, and engineers eager to integrate machine learning into their existing systems efficiently. It's suitable for readers who may lack advanced knowledge of ML but are familiar with programming and basic statistics. By building upon existing skills, it aims to educate its audience on automating and deploying ML models without unnecessary complexity.

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