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Machine Learning with BigQuery ML

You're reading from   Machine Learning with BigQuery ML Create, execute, and improve machine learning models in BigQuery using standard SQL queries

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Product type Paperback
Published in Jun 2021
Publisher Packt
ISBN-13 9781800560307
Length 344 pages
Edition 1st Edition
Languages
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Author (1):
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Alessandro Marrandino Alessandro Marrandino
Author Profile Icon Alessandro Marrandino
Alessandro Marrandino
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Introduction and Environment Setup
2. Chapter 1: Introduction to Google Cloud and BigQuery FREE CHAPTER 3. Chapter 2: Setting Up Your GCP and BigQuery Environment 4. Chapter 3: Introducing BigQuery Syntax 5. Section 2: Deep Learning Networks
6. Chapter 4: Predicting Numerical Values with Linear Regression 7. Chapter 5: Predicting Boolean Values Using Binary Logistic Regression 8. Chapter 6: Classifying Trees with Multiclass Logistic Regression 9. Section 3: Advanced Models with BigQuery ML
10. Chapter 7: Clustering Using the K-Means Algorithm 11. Chapter 8: Forecasting Using Time Series 12. Chapter 9: Suggesting the Right Product by Using Matrix Factorization 13. Chapter 10: Predicting Boolean Values Using XGBoost 14. Chapter 11: Implementing Deep Neural Networks 15. Section 4: Further Extending Your ML Capabilities with GCP
16. Chapter 12: Using BigQuery ML with AI Notebooks 17. Chapter 13: Running TensorFlow Models with BigQuery ML 18. Chapter 14: BigQuery ML Tips and Best Practices 19. Other Books You May Enjoy

Summary

In this chapter, we've learned the most important tips and best practices that we can apply during the implementation of a ML use case with BigQuery ML.

We've analyzed the importance of data preparation; we started looking at the data quality aspects; then, we've learned how we can easily split the data to get balanced training, validation, and test sets.

We then looked at how we can further improve a ML model's performance using BigQuery ML functions for feature engineering.

After that, we focused our attention on tuning hyperparameters. When we train a model, BigQuery ML allows us to choose different parameters, and these variables influence the training stage.

Finally, we have understood why it is so important to deploy BigQuery ML models on other platforms so that we get online predictions and satisfy near-real-time business scenarios.

Congratulations on finishing reading the book! You should now be able to use BigQuery ML for your business...

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