Low-Code AI A Practical Project-Driven Introduction to Machine Learning

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
      1. Retail: Product Pricing
      2. Healthcare: Heart Disease Campaign
      3. Energy: Utility Campaign
      4. Insurance: Advertising Media Channel Sales Prediction
      5. Financial: Fraud Detection
      6. Energy: Power Production Prediction
      7. Telecommunications: Customer Churn Prediction
      8. 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
         1. Sign up for a new GitHub account
         2. Set up your project’s GitHub repo
      Using Google’s Colaboratory for Low-Code AI Projects
         1. Create a Colaboratory Python Jupyter Notebook
         2. Import libraries and dataset using Pandas
         3. Data validation
         4. A little bit of exploratory data analysis
   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
         Google’s BigQuery ML
         Amazon Aurora ML and Redshift ML
      Open Source ML Libraries
         AutoKeras
         Auto-Sklearn
         Auto-PyTorch
   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
         Descriptive analysis: Check the data
         Explore the data
            Heat maps (correlations)
            Scatterplots
            Histogram distribution plot
         Export 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
         Descriptive analysis
         Exploratory analysis
      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
         Using the Null function to check for null values
         Using the Min and Max functions to determine acceptable data ranges
         Saving query results using a DDL statement in BigQuery
   Linear Regression Models
      Feature Selection and Correlation
      Google Colaboratory
      Plotting Feature Relationships to the Label
      The CREATE MODEL Statement in BigQuery ML
         Using the CREATE MODEL statement
         View evaluation metrics of the trained model
         Using the ML.PREDICT function to serve predictions
   Introducing Explainable AI
      Explainable AI in BigQuery ML
         Modifying the CREATE MODEL statement
         Using the ML.GLOBAL_EXPLAIN function
         Using the ML.EXPLAIN_PREDICT function to compute local explanations
      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
         Checking and converting data types
         Exploring summary statistics
         Exploring combinations of categorical columns
         Exploring interactions between numeric and categorical columns
      Transforming Features Using Pandas and Scikit-Learn
         Feature selection
         Encoding categorical features using scikit-learn
         Generalization and data splitting
   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
         Looking for easy improvements
         Feature crosses
      Hyperparameter Tuning
         Hyperparameter tuning strategies
         Hyperparameter tuning in scikit-learn
   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
         Regularization
         Using hyperparameter tuning in the CREATE MODEL statement
   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
         Working with image data
         Working with text data
      Generative AI
      Explainable AI
   ML Operations
   Continuous Training and Evaluation
   Summary
Index