Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries
暫譯: 使用 BigQuery ML 的機器學習：利用標準 SQL 查詢創建、執行和改進 BigQuery 中的機器學習模型
Marrandino, Alessandro
- 出版商: Packt Publishing
- 出版日期: 2021-06-11
- 售價: $2,000
- 貴賓價: 9.5 折 $1,900
- 語言: 英文
- 頁數: 344
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1800560303
- ISBN-13: 9781800560307
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相關分類:
SQL、Machine Learning
海外代購書籍(需單獨結帳)
相關主題
商品描述
Key Features
- Gain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery ML
- Leverage SQL syntax to train, evaluate, test, and use ML models
- Discover how BigQuery works and understand the capabilities of BigQuery ML using examples
Book Description
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.
What you will learn
- Discover how to prepare datasets to build an effective ML model
- Forecast business KPIs by leveraging various ML models and BigQuery ML
- Build and train a recommendation engine to suggest the best products for your customers using BigQuery ML
- Develop, train, and share a BigQuery ML model from previous parts with AI Platform Notebooks
- Find out how to invoke a trained TensorFlow model directly from BigQuery
- Get to grips with BigQuery ML best practices to maximize your ML performance
Who this book is for
This book is for data scientists, data analysts, data engineers, and anyone looking to get started with Google's BigQuery ML. You'll also find this book useful if you want to accelerate the development of ML models or if you are a business user who wants to apply ML in an easy way using SQL. Basic knowledge of BigQuery and SQL is required.
商品描述(中文翻譯)
#### 主要特點
- 清楚了解 GCP 上的 AI 和機器學習服務，學習何時使用這些服務，並了解如何將它們與 BigQuery ML 整合
- 利用 SQL 語法訓練、評估、測試和使用 ML 模型
- 探索 BigQuery 的運作方式，並通過範例了解 BigQuery ML 的功能
#### 書籍描述
BigQuery ML 使您能夠輕鬆地使用 SQL 建立機器學習 (ML) 模型，而無需大量編碼。本書將幫助您加速使用 BigQuery ML 的 ML 模型的開發和部署。
本書首先快速概述 Google Cloud 和 BigQuery 架構。接著，您將學習如何配置 Google Cloud 專案，了解 BigQuery 的架構組件和功能，並了解如何使用 BigQuery ML 建立 ML 模型。本書教您如何在 BigQuery 上使用 SQL 進行 ML。您將分析 ML 模型生命周期的關鍵階段，並掌握用於訓練、評估、測試和使用模型的 SQL 語句。隨著進展，您將通過應用不同的 ML 技術（如線性回歸、二元和多類邏輯回歸、k-means、ARIMA 時間序列、深度神經網絡和 XGBoost）來建立一系列使用案例。接下來，您將使用 BigQuery ML 的功能涵蓋矩陣分解和深度神經網絡。最後，您將探索 BigQuery ML 與其他 Google Cloud Platform 組件（如 AI Platform Notebooks 和 TensorFlow）的整合，並發現超參數調整和性能增強的最佳實踐和技巧。
在本書結束時，您將能夠使用 BigQuery ML 建立和評估自己的 ML 模型。
#### 您將學到什麼
- 探索如何準備數據集以建立有效的 ML 模型
- 通過利用各種 ML 模型和 BigQuery ML 預測業務 KPI
- 使用 BigQuery ML 建立和訓練推薦引擎，為您的客戶建議最佳產品
- 從前面的部分開發、訓練並分享一個 BigQuery ML 模型，並使用 AI Platform Notebooks
- 了解如何直接從 BigQuery 調用訓練好的 TensorFlow 模型
- 掌握 BigQuery ML 的最佳實踐，以最大化您的 ML 性能
#### 本書適合誰
本書適合數據科學家、數據分析師、數據工程師以及任何希望開始使用 Google 的 BigQuery ML 的人。如果您想加速 ML 模型的開發，或者您是一位希望以簡單方式使用 SQL 應用 ML 的商業用戶，您也會發現本書非常有用。需要具備基本的 BigQuery 和 SQL 知識。
作者簡介
Alessandro Marrandino is a Google Cloud customer engineer. He helps various enterprises in the digital transformation journey through the adoption of cloud technologies. He is actively focused on and experienced in data management and smart analytics solutions. He has spent his entire career on data and artificial intelligence projects for global companies in different industries.
作者簡介(中文翻譯)
Alessandro Marrandino 是一位 Google Cloud 的客戶工程師。他協助各種企業在數位轉型過程中採用雲端技術。他專注於數據管理和智慧分析解決方案，並在這方面擁有豐富的經驗。他的整個職業生涯都致力於為不同產業的全球公司進行數據和人工智慧項目。
目錄大綱
Table of Contents
1.Introduction to Google Cloud and BigQuery
2.Setting Up Your GCP and BigQuery Environment
3.Introducing BigQuery Syntax
4.Predicting Numerical Values with Linear Regression
5.Predicting Boolean Values Using Binary Logistic Regression
6.Classifying Trees with Multiclass Logistic Regression
7.Clustering Using the K-Means Algorithm
8.Forecasting Using Time Series
9.Suggesting the Right Product by Using Matrix Factorization
10.Predicting Boolean Values Using XGBoost
11.Implementing Deep Neural Networks
12.Using BigQuery ML with AI Notebooks
13.Running TensorFlow Models with BigQuery ML
14.BigQuery ML Tips and Best Practices
目錄大綱(中文翻譯)
Table of Contents
1.Introduction to Google Cloud and BigQuery
2.Setting Up Your GCP and BigQuery Environment
3.Introducing BigQuery Syntax
4.Predicting Numerical Values with Linear Regression
5.Predicting Boolean Values Using Binary Logistic Regression
6.Classifying Trees with Multiclass Logistic Regression
7.Clustering Using the K-Means Algorithm
8.Forecasting Using Time Series
9.Suggesting the Right Product by Using Matrix Factorization
10.Predicting Boolean Values Using XGBoost
11.Implementing Deep Neural Networks
12.Using BigQuery ML with AI Notebooks
13.Running TensorFlow Models with BigQuery ML
14.BigQuery ML Tips and Best Practices
