- Machine Learning with BigQuery ML
- Contributors
- About the author
- About the reviewers
- Preface
- 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
- 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
- Section 4: Further Extending Your ML Capabilities with GCP
- Chapter 12: Using BigQuery ML with AI Notebooks
- 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
- Other Books You May Enjoy
This section provides an introduction to machine learning and an overview of the technical tools that will be used in the next sections of the book: Google Cloud Platform, BigQuery, and BigQuery ML, as well as the SQL syntax related to it.
This section comprises the following chapters:
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