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

Discovering K-Means clustering

In this section, we'll understand what unsupervised learning is and we'll learn the basics of the K-Means clustering technique.

K-Means is an unsupervised learning algorithm that solves clustering problems. This technique is used to classify data into a set of classes. The letter k represents the number of clusters that are fixed a priori. For our business scenario, we'll use three different clusters.

Important note

While supervised learning is based on a prior knowledge of what the output values of labels should be in a training dataset, unsupervised learning does not leverage labeled datasets. Its goal is to infer the structure of data within a training dataset, without any prior knowledge of it.

Each cluster of data is characterized by a centroid. The centroid represents the midpoint of the cluster and is identified during the training stage and according to the features of the model.

After the training of the K-Means...