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Index
Machine Learning with BigQuery ML
Contributors
About the author
About the reviewers
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Code in Action
Download the color images
Conventions used
Get in touch
Reviews
Section 1: Introduction and Environment Setup
Chapter 1: Introduction to Google Cloud and BigQuery
Introducing Google Cloud Platform
Interacting with GCP
Discovering GCP's key differentiators
Exploring AI and ML services on GCP
Core platform services
Building blocks
Solutions
Introducing BigQuery
BigQuery architecture
BigQuery's advantages over traditional data warehouses
Interacting with BigQuery
BigQuery data structures
Discovering BigQuery ML
BigQuery ML benefits
BigQuery ML algorithms
Understanding BigQuery pricing
BigQuery pricing
BigQuery ML pricing
Free operations and free tiers
Pricing calculator
Summary
Further resources
Chapter 2: Setting Up Your GCP and BigQuery Environment
Technical requirements
Creating your GCP account and project
Registering a GCP account
Exploring Google Cloud Console
Creating a GCP project
Activating BigQuery
Discovering the BigQuery web UI
Exploring the BigQuery public datasets
Searching for a public dataset
Analyzing a table
Summary
Further reading
Chapter 3: Introducing BigQuery Syntax
Technical requirements
Creating a BigQuery dataset
Discovering BigQuery SQL
CRUD operations
Diving into BigQuery ML
Summary
Further resources
Section 2: Deep Learning Networks
Chapter 4: Predicting Numerical Values with Linear Regression
Technical requirements
Introducing the business scenario
Discovering linear regression
Exploring and understanding the dataset
Understanding the data
Checking the data's quality
Segmenting the dataset
Training the linear regression model
Evaluating the linear regression model
Utilizing the linear regression model
Drawing business conclusions
Summary
Further reading
Chapter 5: Predicting Boolean Values Using Binary Logistic Regression
Technical requirements
Introducing the business scenario
Discovering binary logistic regression
Exploring and understanding the dataset
Understanding the data
Segmenting the dataset
Training the binary logistic regression model
Evaluating the binary logistic regression model
Using the binary logistic regression model
Drawing business conclusions
Summary
Further resources
Chapter 6: Classifying Trees with Multiclass Logistic Regression
Technical requirements
Introducing the business scenario
Discovering multiclass logistic regression
Exploring and understanding the dataset
Understanding the data
Checking the data quality
Segmenting the dataset
Training the multiclass logistic regression model
Evaluating the multiclass logistic regression model
Using the multiclass logistic regression model
Drawing business conclusions
Summary
Further resources
Section 3: Advanced Models with BigQuery ML
Chapter 7: Clustering Using the K-Means Algorithm
Technical requirements
Introducing the business scenario
Discovering K-Means clustering
Exploring and understanding the dataset
Understanding the data
Checking the data quality
Creating the training datasets
Training the K-Means clustering model
Evaluating the K-Means clustering model
Using the K-Means clustering model
Drawing business conclusions
Summary
Further resources
Chapter 8: Forecasting Using Time Series
Technical requirements
Introducing the business scenario
Discovering time series forecasting
Exploring and understanding the dataset
Understanding the data
Checking the data quality
Creating the training dataset
Training the time series forecasting model
Evaluating the time series forecasting model
Using the time series forecasting model
Presenting the forecast
Summary
Further resources
Chapter 9: Suggesting the Right Product by Using Matrix Factorization
Technical requirements
Introducing the business scenario
Discovering matrix factorization
Configuring BigQuery Flex Slots
Exploring and preparing the dataset
Understanding the data
Creating the training dataset
Training the matrix factorization model
Evaluating the matrix factorization model
Using the matrix factorization model
Drawing business conclusions
Summary
Further resources
Chapter 10: Predicting Boolean Values Using XGBoost
Technical requirements
Introducing the business scenario
Discovering the XGBoost Boosted Tree classification model
Exploring and understanding the dataset
Checking the data quality
Segmenting the dataset
Training the XGBoost classification model
Evaluating the XGBoost classification model
Using the XGBoost classification model
Drawing business conclusions
Summary
Further resources
Chapter 11: Implementing Deep Neural Networks
Technical requirements
Introducing the business scenario
Discovering DNNs
DNNs in BigQuery ML
Preparing the dataset
Training the DNN models
Evaluating the DNN models
Using the DNN models
Drawing business conclusions
Deep neural networks versus linear models
Summary
Further resources
Section 4: Further Extending Your ML Capabilities with GCP
Chapter 12: Using BigQuery ML with AI Notebooks
Technical requirements
Discovering AI Platform Notebooks
AI Platform Notebooks pricing
Configuring the first notebook
Implementing BigQuery ML models within notebooks
Compiling the AI notebook
Running the code in the AI notebook
Summary
Further resources
Chapter 13: Running TensorFlow Models with BigQuery ML
Technical requirements
Introducing TensorFlow
Discovering the relationship between BigQuery ML and TensorFlow
Understanding commonalities and differences
Collaborating with BigQuery ML and TensorFlow
Converting BigQuery ML models into TensorFlow
Training the BigQuery ML to export it
Exporting the BigQuery ML model
Running TensorFlow models with BigQuery ML
Summary
Further resources
Chapter 14: BigQuery ML Tips and Best Practices
Choosing the right BigQuery ML algorithm
Preparing the datasets
Working with high-quality data
Segmenting the datasets
Understanding feature engineering
Tuning hyperparameters
Using BigQuery ML for online predictions
Summary
Further resources
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