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)
Section 1: Introduction and Environment Setup
Section 2: Deep Learning Networks
Section 3: Advanced Models with BigQuery ML
Section 4: Further Extending Your ML Capabilities with GCP

Chapter 7: Clustering Using the K-Means Algorithm

In this chapter, we'll introduce unsupervised machine learning, and you'll learn how to use BigQuery ML to build K-Means algorithms to cluster similar data into multiple categories.

Unsupervised machine learning is particularly useful when we have datasets without any labels, and we need to infer the structure of the data without any initial knowledge.

In different industries, it can be very valuable to identify similar events, objects, and people according to a specific set of features. K-Means clustering is typically used to identify similar customers, documents, products, events, or items according to a specific set of characteristics.

In this chapter, we'll focus our attention on the K-Means clustering algorithm, which is widely used to reveal similarities in structured and unstructured data. We'll go through all the steps required to build a K-Means clustering model, leveraging BigQuery ML.