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.
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.
A problem solver at heart, 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.