book

Low-Code AI

by Gwendolyn Stripling, Michael Abel

September 2023

Intermediate to advanced

328 pages

8h 47m

English

O'Reilly Media, Inc.

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Includes

Quizzes

Preface
Who Should Read This Book?What Is and Isn’t in This BookConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
1. How Data Drives Decision Making in Machine Learning
What Is the Goal or Use Case?An Enterprise ML WorkflowDefining the Business Objective or Problem StatementData CollectionData PreprocessingData AnalysisData Transformation and Feature SelectionResearching the Model Selection or Using AutoML (a No-Code Solution)Model Training, Evaluation, and TuningModel TestingModel Deployment (Serving)Maintaining ModelsSummary
2. Data Is the First Step
Overview of Use Cases and Datasets Used in the Book1. Retail: Product Pricing2. Healthcare: Heart Disease Campaign3. Energy: Utility Campaign4. Insurance: Advertising Media Channel Sales Prediction5. Financial: Fraud Detection6. Energy: Power Production Prediction7. Telecommunications: Customer Churn Prediction8. Automotive: Improve Custom Model PerformanceData and File TypesQuantitative and Qualitative DataStructured, Unstructured, and Semistructured DataData File TypesHow Data Is ProcessedAn Overview of GitHub and Google’s ColabUse GitHub to Create a Data Repository for Your ProjectsUsing Google’s Colaboratory for Low-Code AI ProjectsSummary
3. Machine Learning Libraries and Frameworks
No-Code AutoMLHow AutoML WorksMachine Learning as a ServiceLow-Code ML FrameworksSQL ML FrameworksOpen Source ML LibrariesSummary
4. Use AutoML to Predict Advertising Media Channel Sales
The Business Use Case: Media Channel Sales PredictionProject WorkflowProject DatasetExploring the Dataset Using Pandas, Matplotlib, and SeabornLoad Data into a Pandas DataFrame in a Google Colab NotebookExplore the Advertising DatasetUse AutoML to Train a Linear Regression ModelNo-Code Using Vertex AICreate a Managed Dataset in Vertex AISelect the Model ObjectiveBuild the Training ModelEvaluate Model PerformanceModel Feature Importance (Attribution)Get Predictions from Your ModelSummary
5. Using AutoML to Detect Fraudulent Transactions
The Business Use Case: Fraud Detection for Financial TransactionsProject WorkflowProject DatasetExploring the Dataset Using Pandas, Matplotlib, and SeabornLoading Data into a Pandas DataFrame in a Google Colab NotebookExploring the DatasetExporting the DatasetClassification Models and MetricsUsing AutoML to Train a Classification ModelCreating a Managed Dataset and Selecting the Model ObjectiveExploring Dataset StatisticsTraining the ModelEvaluating Model PerformanceModel Feature ImportancesGetting Predictions from Your ModelSummary
6. Using BigQuery ML to Train a Linear Regression Model
The Business Use Case: Power Plant ProductionCleaning the Dataset Using SQL in BigQueryLoading a Dataset into BigQueryExploring Data in BigQuery Using SQLLinear Regression ModelsFeature Selection and CorrelationGoogle ColaboratoryPlotting Feature Relationships to the LabelThe CREATE MODEL Statement in BigQuery MLIntroducing Explainable AIExplainable AI in BigQuery MLExercisesNeural Networks in BigQuery MLBrief Overview of Neural NetworksActivation Functions and NonlinearityTraining a Deep Neural Network in BigQuery MLExercisesDeep Dive: Using Cloud Shell to View Your Cloud Storage FileSummary
7. Training Custom ML Models in Python
The Business Use Case: Customer Churn PredictionChoosing Among No-Code, Low-Code, or Custom Code ML SolutionsExploring the Dataset Using Pandas, Matplotlib, and SeabornLoading Data into a Pandas DataFrame in a Google Colab NotebookUnderstanding and Cleaning the Customer Churn DatasetTransforming Features Using Pandas and Scikit-LearnBuilding a Logistic Regression Model Using Scikit-LearnLogistic RegressionTraining and Evaluating a Model in Scikit-LearnClassification Evaluation MetricsServing Predictions with a Trained Model in Scikit-LearnPipelines in Scikit-Learn: An IntroductionBuilding a Neural Network Using KerasIntroduction to KerasTraining a Neural Network Classifier Using KerasBuilding Custom ML Models on Vertex AISummary
8. Improving Custom Model Performance
The Business Use Case: Used Car Auction PricesModel Improvement in Scikit-LearnLoading the Notebook with the Preexisting ModelLoading the Datasets and the Training-Validation-Test Data SplitExploring the Scikit-Learn Linear Regression ModelFeature Engineering and Improving the Preprocessing PipelineHyperparameter TuningModel Improvement in KerasIntroduction to Preprocessing Layers in KerasCreating the Dataset and Preprocessing Layers for Your ModelBuilding a Neural Network ModelHyperparameter Tuning in KerasHyperparameter Tuning in BigQuery MLLoading and Transforming Car Auction DataTraining a Linear Regression Model and Using the TRANSFORM ClauseConfigure a Hyperparameter Tuning Job in BigQuery MLOptions for Hyperparameter Tuning Large ModelsVertex AI Training and TuningAutomatic Model Tuning with Amazon SageMakerAzure Machine LearningSummary
9. Next Steps in Your AI Journey
Going Deeper into Data ScienceWorking with Unstructured DataGenerative AIExplainable AIML OperationsContinuous Training and EvaluationSummary

Index
About the Authors

Overview

Take a data-first and use-case-driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn key ML concepts by using real-world datasets with realistic problems.

Business and data analysts get a project-based introduction to ML/AI using a detailed, data-driven approach: loading and analyzing data; feeding data into an ML model; building, training, and testing; and deploying the model into production. Authors Michael Abel and Gwendolyn Stripling show you how to build machine learning models for retail, healthcare, financial services, energy, and telecommunications.

You'll learn how to:

Distinguish between structured and unstructured data and the challenges they present
Visualize and analyze data
Preprocess data for input into a machine learning model
Differentiate between the regression and classification supervised learning models
Compare different ML model types and architectures, from no code to low code to custom training
Design, implement, and tune ML models
Export data to a GitHub repository for data management and governance

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