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low-code-ai

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Title: Low-Code AI

Table of Contents

0. Preface

   • Who Should Read This Book?
   • What Is and Isn’t in This Book
   • Conventions Used in This Book
   • Using Code Examples
   • O’Reilly Online Learning
   • How to Contact Us
   • Acknowledgments

1. How Data Drives Decision Making in Machine Learning

   • What Is the Goal or Use Case?
   • An Enterprise ML Workflow
     • Defining the Business Objective or Problem Statement
     • Data Collection
     • Data Preprocessing
     • Data Analysis
     • Data Transformation and Feature Selection
     • Researching the Model Selection or Using AutoML (a No-Code Solution)
     • Model Training, Evaluation, and Tuning
     • Model Testing
     • Model Deployment (Serving)
     • Maintaining Models
   • Summary

2. Data Is the First Step

   • Overview of Use Cases and Datasets Used in the Book
     • Retail: Product Pricing
     • Healthcare: Heart Disease Campaign
     • Energy: Utility Campaign
     • Insurance: Advertising Media Channel Sales Prediction
     • Financial: Fraud Detection
     • Energy: Power Production Prediction
     • Telecommunications: Customer Churn Prediction
     • Automotive: Improve Custom Model Performance
   • Data and File Types
     • Quantitative and Qualitative Data
     • Structured, Unstructured, and Semistructured Data
     • Data File Types
     • How Data Is Processed
   • An Overview of GitHub and Google’s Colab
     • Use GitHub to Create a Data Repository for Your Projects
     • Using Google’s Colaboratory for Low-Code AI Projects
   • Summary

3. Machine Learning Libraries and Frameworks

   • No-Code AutoML
   • How AutoML Works
   • Machine Learning as a Service
   • Low-Code ML Frameworks
     • SQL ML Frameworks
     • Open Source ML Libraries
   • Summary

4. Use AutoML to Predict Advertising Media Channel Sales

   • The Business Use Case: Media Channel Sales Prediction
     • Project Workflow
     • Project Dataset
   • Exploring the Dataset Using Pandas, Matplotlib, and Seaborn
     • Load Data into a Pandas DataFrame in a Google Colab Notebook
     • Explore the Advertising Dataset
     • Use AutoML to Train a Linear Regression Model
     • No-Code Using Vertex AI
     • Create a Managed Dataset in Vertex AI
     • Select the Model Objective
     • Build the Training Model
     • Evaluate Model Performance
     • Model Feature Importance (Attribution)
     • Get Predictions from Your Model
   • Summary

5. Using AutoML to Detect Fraudulent Transactions

   • The Business Use Case: Fraud Detection for Financial Transactions
     • Project Workflow
     • Project Dataset
   • Exploring the Dataset Using Pandas, Matplotlib, and Seaborn
   • Loading Data into a Pandas DataFrame in a Google Colab Notebook * Exploring the Dataset * Exporting the Dataset
   • Classification Models and Metrics
   • Using AutoML to Train a Classification Model
     • Creating a Managed Dataset and Selecting the Model Objective
     • Exploring Dataset Statistics
     • Training the Model
     • Evaluating Model Performance
     • Model Feature Importances
     • Getting Predictions from Your Model
   • Summary

6. Using BigQuery ML to Train a Linear Regression Model

   • The Business Use Case: Power Plant Production
   • Cleaning the Dataset Using SQL in BigQuery
     • Loading a Dataset into BigQuery
     • Exploring Data in BigQuery Using SQL
   • Linear Regression Models
     • Feature Selection and Correlation
     • Google Colaboratory
     • Plotting Feature Relationships to the Label
     • The CREATE MODEL Statement in BigQuery ML
   • Introducing Explainable AI
     • Explainable AI in BigQuery ML
     • Exercises
   • Neural Networks in BigQuery ML
     • Brief Overview of Neural Networks
     • Activation Functions and Nonlinearity
     • Training a Deep Neural Network in BigQuery ML
     • Exercises
   • Deep Dive: Using Cloud Shell to View Your Cloud Storage File
   • Summary

7. Training Custom ML Models in Python

   • The Business Use Case: Customer Churn Prediction
   • Choosing Among No-Code, Low-Code, or Custom Code ML Solutions
   • Exploring the Dataset Using Pandas, Matplotlib, and Seaborn
     • Loading Data into a Pandas DataFrame in a Google Colab Notebook
     • Understanding and Cleaning the Customer Churn Dataset
     • Transforming Features Using Pandas and Scikit-Learn
   • Building a Logistic Regression Model Using Scikit-Learn
     • Logistic Regression
     • Training and Evaluating a Model in Scikit-Learn
     • Classification Evaluation Metrics
     • Serving Predictions with a Trained Model in Scikit-Learn
     • Pipelines in Scikit-Learn: An Introduction
   • Building a Neural Network Using Keras
     • Introduction to Keras
     • Training a Neural Network Classifier Using Keras
     • Building Custom ML Models on Vertex AI
   • Summary

8. Improving Custom Model Performance

   • The Business Use Case: Used Car Auction Prices
   • Model Improvement in Scikit-Learn
     • Loading the Notebook with the Preexisting Model
     • Loading the Datasets and the Training-Validation-Test Data Split
     • Exploring the Scikit-Learn Linear Regression Model
     • Feature Engineering and Improving the Preprocessing Pipeline
     • Hyperparameter Tuning
   • Model Improvement in Keras
     • Introduction to Preprocessing Layers in Keras
     • Creating the Dataset and Preprocessing Layers for Your Model
     • Building a Neural Network Model
     • Hyperparameter Tuning in Keras
   • Hyperparameter Tuning in BigQuery ML
     • Loading and Transforming Car Auction Data
     • Training a Linear Regression Model and Using the TRANSFORM Clause
     • Configure a Hyperparameter Tuning Job in BigQuery ML
   • Options for Hyperparameter Tuning Large Models
     • Vertex AI Training and Tuning
     • Automatic Model Tuning with Amazon SageMaker
     • Azure Machine Learning
   • Summary

9. Next Steps in Your AI Journey

   • Going Deeper into Data Science
     • Working with Unstructured Data
     • Generative AI
     • Explainable AI
   • ML Operations
   • Continuous Training and Evaluation
   • Summary