Smart Analytics, Machine Learning, and AI on Google Cloud (edX)

For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions using Vertex AI. Learners will get hands-on experience building machine learning models on Google Cloud using QwikLabs.
This course is part of the Google Cloud Data Engineer Learning Path Professional Certificate.

What you'll learn

• Differentiate between ML, AI and Deep Learning.
• Discuss the use of ML API’s on unstructured data.
• Execute BigQuery commands from Notebooks.
• Create ML models by using SQL syntax in BigQuery.
• Create ML models without coding using AutoML.

Syllabus

1. Introduction

In this module, we introduce the course and agenda.

1. Introduction to Analytics and AI

This module talks about ML options on Google Cloud.

1. Prebuilt ML Model APIs for Unstructured Data

This module focuses on using pre-built ML APIs on your unstructured data.

1. Big Data Analytics with Notebooks

This module covers how to use Notebooks.

1. Production ML Pipelines

This module covers building custom ML models and introduces Vertex AI and AI Hub.

1. Custom Model Building with SQL in BigQuery ML

This module covers BigQuery ML.

1. Custom Model Building with AutoML

Custom model building with AutoML.

1. Summary

This module recaps the topics covered in the course.

1. Course Resources

PDF links to all modules.