Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries
genuine image
Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries
BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding.
Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries
Nº de artículo: 53325318

Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries

85% of respondents would recommend this to a friend

Nº de artículo: 53325318

USD 68

Price Details

Excluding Shipping & Custom charges ( Shipping and custom charges will be calculated on checkout )

*All items will import from Estados Unidos

En stock
Estados Unidos Importado de la tienda USA

QTY:

Haz tu pedido ahora y recíbelo por ahí Viernes, Octubre 09
Secured transactionsecured transaction

Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.

Our Top Logistics Partners
  • fedex
  • dhl
BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding.
Mostrar más
Garantía U-Care:
Ninguno
Selecciona un plan
fast shipping

Fast
Shipping

free return

Free
Return*

secure packaging

Secure Packaging

100% original products

100% Original Products

pci-dss

PCI DSS Compliance

iso certified

ISO 27001 Certified

paypal payment
visa payment
mastercard payment

What Stands Out

SQL Integration
Utilizes standard SQL queries, making it accessible for users familiar with SQL, thus lowering the barrier for data scientists and analysts to create ML models without complex programming.
Scalability
Harnesses BigQuery's powerful infrastructure to handle vast datasets effortlessly, enabling the analysis and training of machine learning models on large-scale data without performance issues.
Model Improvement
Offers built-in capabilities to refine and optimize ML models, allowing users to iteratively enhance model accuracy and effectiveness based on real-time data feedback and performance metrics.

Detalles de producto

Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries
  • Gain a clear understanding of AI and machine learning services on GCP and learn how to integrate them with BigQuery ML
  • Leverage SQL syntax to train, evaluate, test, and use ML models
  • Discover how to prepare datasets to build an effective ML model
  • Build and train a recommendation engine to suggest the best products for customers using BigQuery ML
  • Explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow
  • This book is for data scientists, data analysts, data engineers, and anyone looking to get started with Google's BigQuery ML
Publisher Packt Publishing
Publication date June 11, 2021
Language English
Print length 344 pages
ISBN-10 1800560303
ISBN-13 978-1800560307
Item Weight 1.3 pounds (590 grams)
Dimensions 7.5 x 0.78 x 9.25 inches (19.1 x 2 x 23.5 cm)

Who Should Buy?

Suitable For
  • Data Analysts

    Data analysts looking to leverage machine learning without requiring extensive programming knowledge will find BigQuery ML advantageous.

  • Business Intelligence

    Organizations focused on integrating predictive analytics into their BI workflows can use BigQuery ML effectively with existing SQL skills.

  • Data Scientists

    Data scientists seeking a scalable and efficient platform to deploy machine learning models can benefit significantly from BigQuery ML.

Not Suitable For
  • Non-Technical Users

    Individuals with no SQL knowledge may struggle with BigQuery ML despite its user-friendly approach to machine learning.

DESCRIPCIÓN DEL PRODUCTO

¿Tienes alguna consulta? Chatea con nosotros

Preguntas y respuestas de los clientes

  • Pregunta: What is BigQuery ML and how does it relate to machine learning?

    Respuesta: BigQuery ML is a feature within Google BigQuery that enables users to create and run machine learning models directly using standard SQL queries. This integration simplifies the process, allowing data analysts and other non-experts to leverage machine learning capabilities without needing extensive coding skills. By utilizing familiar SQL syntax, users can create models for classification, regression, and more—allowing businesses to derive insights from their data quickly. For example, a marketing team can use BigQuery ML to analyze customer behavior and predict future buying trends effectively.
  • Pregunta: Can I perform training and evaluation of machine learning models in BigQuery?

    Respuesta: Yes, you can train and evaluate machine learning models directly in BigQuery. BigQuery ML allows users to create models and use the dataset for training, while also providing built-in evaluation metrics. This is beneficial for understanding model efficacy. For instance, if a data scientist builds a model to predict product demand, they can assess accuracy metrics right within BigQuery, making iterative improvements simpler and more efficient.
  • Pregunta: What types of machine learning models can be created with BigQuery ML?

    Respuesta: BigQuery ML supports various types of machine learning models, including linear regression, logistic regression, k-means clustering, and TensorFlow models. This versatility allows users from different domains to solve a variety of challenges, such as predicting sales, classifying data, or even clustering customers based on behavior. For example, a retail company could use k-means clustering to segment its customer base for targeted marketing campaigns.
  • Pregunta: Is it necessary to have coding experience to use BigQuery ML?

    Respuesta: No coding experience is necessary to use BigQuery ML, as it is designed to be user-friendly and accessible to those familiar with SQL. This opens the door for data analysts and business users who want to implement machine learning solutions without delving into more complex programming languages like Python. A business analyst could quickly create a predictive model for sales forecasting using standard SQL queries, streamlining the decision-making process.
  • Pregunta: How can I improve the performance of my machine learning models in BigQuery?

    Respuesta: Improving the performance of machine learning models in BigQuery can be achieved through several methods, such as feature engineering, hyperparameter tuning, and iterative training. Utilizing additional features or modifying existing ones can enhance the model’s predictive power. For instance, if you’re developing a model to forecast sales, including seasonal trends or promotional periods as features could significantly improve accuracy, enabling businesses to make better-informed decisions.
  • Pregunta: What are some use cases for Machine Learning with BigQuery ML?

    Respuesta: There are numerous practical use cases for BigQuery ML, including customer churn prediction, fraud detection, inventory forecasting, and recommendation systems. For example, an e-commerce platform could leverage machine learning to analyze user purchase history and recommend products, thereby enhancing the shopping experience and boosting sales. Each use case allows organizations to harness their data for meaningful insights and growth.
  • Pregunta: What data formats can I use in BigQuery for machine learning?

    Respuesta: BigQuery supports various data formats, including CSV, JSON, and Avro, making it flexible for ingesting and processing data. You can directly import your datasets into BigQuery and also utilize existing tables within your BigQuery project for machine learning. For instance, if you have user clickstream data in CSV format, you can upload it into BigQuery, and then use it to train a model for predicting user engagement.
  • Pregunta: How does BigQuery ML integrate with other Google Cloud services?

    Respuesta: BigQuery ML integrates seamlessly with other Google Cloud services such as Google Cloud Storage, Cloud Functions, and AI Platform. This interoperability allows you to pull data from a variety of sources or export your machine learning models for further deployment. For example, after building a model in BigQuery, you could use Google Cloud Functions to trigger actions based on predictions, enhancing your automated workflows.
  • Pregunta: What role does SQL play in creating models with BigQuery ML?

    Respuesta: SQL plays a central role in BigQuery ML, enabling users to write queries that directly create and train machine learning models. This integration allows users to leverage their existing SQL skills to perform complex operations without needing to switch to another programming environment. For example, a data analyst can simply write a SQL statement to create a predictive model, decreasing the barrier to entry for machine learning initiatives in a company.
  • Pregunta: Where can I buy Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries?

    Respuesta: You can buy 'Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries' through Ubuy in El Salvador. Ubuy offers a convenient platform for accessing this resource, enabling you to enhance your knowledge and skills in machine learning with BigQuery, right at your fingertips.

Intelligence & Semantics Editorial Review

  • ubuy El Salvador
  • ubuy El Salvador
  • ubuy El Salvador
  • ubuy El Salvador
  • ubuy El Salvador
  • ubuy El Salvador
  • ubuy El Salvador
  • ubuy El Salvador
  • ubuy El Salvador
  • ubuy El Salvador

**** "Machine Learning with BigQuery ML" is an insightful guide dedicated to harnessing the power of BigQuery for machine learning through standard SQL queries. Designed to cater to both newcomers and those familiar with Google Cloud Platform, the book expedites the learning process with a step-by-step approach. Readers praise its clarity, structured format, and practical examples that bridge the gap between data analytics and machine learning, allowing users to draw meaningful insights without heavy reliance on programming languages like Python or R. The initial sections of the book introduce readers to the BigQuery and Google Cloud Platform environments before diving into foundational to advanced machine learning concepts, including linear regression, classification, and sophisticated algorithms like K-means and XGBoost. The inclusion of relevant code snippets, up-to-date UI images, and access to free datasets further enrich the learning experience, proving valuable to business intelligence professionals seeking to enhance their skillsets. Users appreciate the book's ability to demystify BigQuery machine learning, making it all the more accessible for those with a background in SQL. It holds particular appeal for professionals in Adtech, Martech, and customer service sectors, where machine learning is gaining traction. The book's practical use cases, well-structured flow, and references to resources for further learning make it an essential addition for anyone looking to deepen their expertise in BigQuery and its machine learning capabilities. However, there are minor critiques, such as the need for an update on a typo and suggestions for additional data prep tools. Overall, the book has garnered praise for its ease of use and comprehensiveness, establishing itself as a crucial guide for both budding data scientists and seasoned analysts eager to leverage machine learning in their workflows. **

Customer Reviews & Ratings

12 valoraciones de los clientes
  • 5 estrella
    90%
  • 4 estrella
    10%
  • 3 estrella
    0%
  • 2 estrella
    0%
  • 1 estrella
    0%

Revisar este producto

Comparte tus ideas con otros clientes

ventajas

  • Easy to read and structured for beginners and experienced users alike.
  • Comprehensive coverage of machine learning using SQL in BigQuery.
  • Step-by-step guides to data preparation, model training, evaluation, and prediction.
  • Practical examples that utilize public datasets familiar to many users.
  • Inclusive of code snippets facilitating hands-on practice.
  • Excellent resource for enhancing machine learning skills without requiring other programming languages.
  • Suitable for professionals in various sectors utilizing BigQuery.

Contras

  • Minor typos present (e.g., a correction needed on page 324 regarding the MOD function).

Platform Trust & Buyer Confidence

trustpilot logo
4.2/5 9477 reseñas
Read reviews
JK
Jasmin
Verified buyer

“Great products and very good service: very easy and very fast international delivery.”

9 September 2026 · via Trustpilot
AG
Anke
Verified buyer

“Wonderful online shopping experience, smooth transaction from the start. Payment method works conveniently and delivery is unexpectedly fast and reliable. You go the extra mile for service. What makes this even more amazing, you deliver to Namibia. I will remain a happy Ubuy customer and will increase my purchases for sure! Thank you!”

10 September 2026 · via Trustpilot
H
Hazel
Verified buyer

“Very easy to find the products what you need, and so fast delivery, that’s why I highly recommended to others costumers to used ubuy.”

10 September 2026 · via Trustpilot
O
Opaleye
Verified buyer

“I received exactly what I ordered I was skeptical about your site because that was my first time to order. But the order came timely and neatly packaged. I was not disappointed. Thank you.”

11 September 2026 · via Trustpilot
AC
Adele
Verified buyer

“Easy to find and order what you want on the website. Delivery is quick to the UK”

8 September 2026 · via Trustpilot
Pago seguro Global Delivery Easy Returns Genuine Products

Product Price History

Información importante

  • Limitaciones: Para los productos enviados al extranjero, ten en cuenta que cualquier garantía del fabricante puede no ser válida; las opciones de servicio del fabricante pueden no estar disponibles; los manuales del producto, las instrucciones y las advertencias de seguridad pueden no estar en los idiomas del país de destino; los productos (y los materiales que los acompañan) pueden no estar diseñados de acuerdo con las normas, especificaciones y requisitos de etiquetado del país de destino; y los productos pueden no ajustarse al voltaje del país de destino y a otras normas eléctricas (lo que requiere el uso de un adaptador o convertidor, si procede). El destinatario es responsable de asegurarse de que el producto puede ser importado legalmente al país de destino. Cuando hagas un pedido a Ubuy o a sus filiales, el destinatario es el importador registrado y debe cumplir todas las leyes y normativas del país de destino.
  • No todos los productos que aparecen en Ubuy están a la venta, ya que Ubuy es un motor de búsqueda a nivel mundial. Los productos están sujetos a las normas de exportación/comercio.

USD 68

Haz tu pedido ahora y recíbelo por ahí Viernes, Octubre 09

QTY:

Secured transactionsecured transaction

PCI DSS compliant and ISO 27001:2022 certified, with encrypted payments and full buyer protection on every order.

Our Top Logistics Partners
  • fedex
  • dhl
Fastest cross-border delivery

características y beneficios

  • Easily build ML models with SQL using BigQuery ML
  • Learn the key phases of ML model's lifecycle using SQL statements
  • Gain expertise in training, evaluating, testing, and using an ML model
  • Develop use cases, hyperparameter tuning, and performance enhancements
  • Maximize ML performance with BigQuery ML best practices

Ubuy Assurance

Experience worry-free shopping with 100% original products, PCI DSS-compliant payment security, ISO 27001-certified data protection, the fastest cross-border delivery, free returns *, and secure packaging on every order.