Low–Code AI

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

Index
About the Authors