Building Machine Learning Models in SQL Using BigQuery ML
BigQuery ML on the Google Cloud Platform democratizes machine learning by allowing data analysts and engineers to build and use machine learning models directly from SQL without using any higher level programming language.
What you'll learn
This course demonstrates how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, the Google Cloud Platform’s serverless data warehouse. In this course, Building Machine Learning Models in SQL Using BigQuery ML, you'll learn how to build and train machine learning models and how to employ those models for prediction - all with just simple SQL commands on data stored in BigQuery. First, you'll understand the different choices available on the GCP if you would like to build and train your models and see how you can make the right choice between these services for your specific use case. Then, you'll work with some real-world datasets stored in BigQuery to build linear regression and binary classification models. Because BigQuery allows you to specify training parameters to build and train your model in SQL, machine learning is made accessible to even those who are not familiar with high-level programming languages. Last, you'll study how to analyze the models that we built using evaluation and feature inspection functions in BigQuery, and run BigQuery commands on Cloud Datalab using a Jupyter notebook that is hosted on the GCP and closely integrated with all of GCPs services. By the end of this course, you'll have a good understanding of how you can use BigQuery ML to extract insights from your data by applying linear and logistic regression models.
Table of contents
- Module Overview 1m
- Prerequisites and Course Outline 3m
- Democratizing Machine Learning with BigQuery ML 4m
- BigQuery ML vs. Other Google AI Services 5m
- Logging into the GCP 2m
- Uploading Reviews to Cloud Shell 2m
- Creating Datasets and Tables, Loading and Querying Data 3m
- Running Queries and Visualizing Results Using Data Studio 4m
- Module Overview 1m
- Linear Regression 2m
- Logistic Regression 4m
- Building Linear and Logistic Regression Models in BigQuery ML 6m
- Creating and Loading a Table with Data 4m
- Creating and Training a Regression Model 4m
- Evaluating the Regression Model 3m
- Predictions and Data Visualization 3m
- Accuracy, Precision and Recall Using the Confusion Matrix 4m
- Creating and Training a Classification Model 4m
- Evaluating the Classifier and Using It for Prediction 5m
About the author
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more