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

Includes Quizzes

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
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
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
No-Code AutoMLHow AutoML WorksMachine Learning as a ServiceLow-Code ML FrameworksSQL ML FrameworksOpen Source ML LibrariesSummary
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
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
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
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
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
Going Deeper into Data ScienceWorking with Unstructured DataGenerative AIExplainable AIML OperationsContinuous Training and EvaluationSummary

Content preview from Low-Code AI

Chapter 7. Training Custom ML Models in Python

In this chapter, you’ll learn how to build classification models to predict customer churn using two popular ML libraries available in Python, scikit-learn and Keras. First, you’ll explore and clean your data using Pandas. Then you’ll learn how to use scikit-learn to prepare categorical features for training using one-hot encoding, train a logistic regression model, understand model performance using evaluation metrics, and improve model performance. You’ll learn how to perform the same steps using Keras to build a neural network classification model using the already prepared data. Along the way, you’ll learn more about performance metrics for classification models and how to better understand a confusion matrix to better evaluate your classification models.

The dataset being used for this chapter, the IBM Telco Customer Churn dataset, is a popular dataset for learning how to model customer churn. You should feel encouraged to look at other examples of how to work with this dataset to grow your knowledge after completing the exercises in this chapter.

The Business Use Case: Customer Churn Prediction

Your goal in this project will be to predict customer churn for a telecommunications company. Customer churn is defined as the attrition rate for customers, or in other words the rate of customers that choose to stop using services. Telecommunications companies often sell their products at a monthly rate or via annual contracts, so ...