Book Image

Machine Learning with BigQuery ML

By : Alessandro Marrandino
Book Image

Machine Learning with BigQuery ML

By: Alessandro Marrandino

Overview of this book

BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML. The book starts with a quick overview of Google Cloud and BigQuery architecture. You'll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement. By the end of this BigQuery book, you'll be able to build and evaluate your own ML models with BigQuery ML.
Table of Contents (20 chapters)
1
Section 1: Introduction and Environment Setup
5
Section 2: Deep Learning Networks
9
Section 3: Advanced Models with BigQuery ML
15
Section 4: Further Extending Your ML Capabilities with GCP

Discovering matrix factorization

In this section, we'll learn what matrix factorization is, and how it can be used to build recommendation engines.

Matrix factorization represents a class of algorithms usually used to build recommendation engines. These algorithms are built on matrices that represent the interactions between users and items. In these kinds of matrices, the following occurs:

  • Each user or customer is represented as a row.
  • Each item or product corresponds to a column of the matrix.
  • Each cell of the matrix is filled with a numeric value: the feedback.

This feedback represents a rating that a specific user has given to a specific item.

In the following screenshot, we can see an example of a matrix where the rows are the customers of a video streaming service and the columns are the films offered by the platform. Some of the cells contain a rating that ranges from 1 to 5:

Figure 9.2 – Representation of a recommendation...