Detailed Course Outline
Module 1 - Introduction to Analytics and AI
Topics:
- What is AI?
- From ad hoc data analysis to data-driven decisions
- Options for ML models on Google Cloud
Objectives:
- Describe the relationship between ML, AI, and deep learning
- Identify ML options on Google Cloud
Module 2 - Prebuilt ML Model APIs for Unstructured Data
Topics:
- The difficulties of unstructured data
- ML APIs for enriching data
Objectives:
- Discuss challenges when working with unstructured data
- Identify ready-to-use ML API’s for unstructured data
Module 3 - Big Data Analytics with Notebooks
Topics:
- Defining notebooks
- BigQuery magic and ties to Pandas
Objectives:
- Introduce notebooks as a tool for prototyping ML solutions.
- Execute BigQuery commands from notebooks.
Module 4 - Production ML Pipelines
Topics:
- Ways to do ML on Google Cloud
- Vertex AI Pipelines
- TensorFlow Hub
Objectives:
- Describe options available for building custom ML models.
- Describe the use of tools like Vertex AI and TensorFlow Hub.
Module 5 - Custom Model Building with SQL in BigQuery ML
Topics:
- BigQuery ML for quick model building
- Supported models
Objectives:
- Create ML models by using SQL syntax in BigQuery.
- Demonstrate building different kinds of ML models by using BigQuery ML.
Module 6 - Custom Model Building with AutoML
Topics:
- Why use AutoML?
- AutoML Vision
- AutoML NLP
- AutoML Tables
Objectives:
- Explore various AutoML products used in machine learning.
- Identify ready-to-use ML API’s for unstructured data.