This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models. The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
Overview
Syllabus
- Introduction to Course and Series
- Course series preview
- Course introduction
- What It Means to be AI-First
- Introduction
- What is ML?
- What problems can it solve?
- Activity intro: Framing a machine learning problem
- Activity: Framing a machine learning problem
- Activity solutions: Framing a machine learning problem
- Infuse your apps with ML
- Build a data strategy around ML
- Quiz: What it Means to be AI First
- Resources: What It Means to Be AI First
- How Google Does ML
- Introduction
- ML surprise
- The secret sauce
- ML and business processes
- The path to ML
- A closer look at the path to ML
- End of phases deep dive
- Quiz: How Google Does ML
- Resources: How Google Does ML
- Machine Learning Development with Vertex AI
- Introduction
- Moving from experimentation to production
- Components of Vertex AI
- Lab intro: Using an image dataset to train an AutoML model
- Lab demo: Using an image dataset to train an AutoML model
- Using an Image Dataset to Train an AutoML Model
- Lab intro: Training an AutoML video classification model
- Lab demo: Training an AutoML video classification model
- Training an AutoML Video Classification Model
- Tools to interact with Vertex AI
- Quiz: Machine Learning Development with Vertex AI
- Resources: Machine Learning Development with Vertex AI
- Machine Learning Development with Vertex Notebooks
- Introduction
- Machine learning development with Vertex Notebooks
- Quiz: Machine Learning Development with Vertex Notebooks
- Resources: Machine Learning Development with Vertex Notebooks
- Best Practices for Implementing Machine Learning on Vertex AI
- Introduction
- Best practices for machine learning development
- Data preprocessing best practices
- Best practices for machine learning environment setup
- Quiz: Best Practices for Implementing Machine Learning on Vertex AI
- Responsible AI Development
- Introduction
- Overview
- Human biases lead to biases in ML models
- Biases in data
- Evaluating metrics with inclusion for your ML system
- Equality of opportunity
- How to find errors in your dataset using Facets
- Quiz: Responsible AI Development
- Resources: Responsible AI Development
- Summary
- Summary
- Resource: All quiz questions
- Resource: All readings
- Resource: All slides
- Your Next Steps
- Course Badge
