Business professionals in non-technical roles have a unique opportunity to lead or influence machine learning projects. If you have questions about machine learning and want to understand how to use it, without the technical jargon, this course is for you. Learn how to translate business problems into machine learning use cases and vet them for feasibility and impact. Find out how you can discover unexpected use cases, recognize the phases of an ML project and considerations within each, and gain confidence to propose a custom ML use case to your team or leadership or translate the requirements to a technical team.
Overview
Syllabus
- Introduction
- Introduction
- Identifying business value for using ML
- Introduction
- AI vs ML vs Deep Learning
- Phase 1: Assess feasibility
- Practice assessing the feasibility of ML use cases
- Module 2: Graded Quiz
- Module 2: Worksheet
- Defining ML as a practice
- Common ML problem types
- Standard algorithm and data
- Data quality
- Predictive insights and decisions
- More ML examples
- Practice series: Analyze the ML use case
- Module 3: Worksheet
- Saving the world's bees
- Google Assistant for accessibility
- Exercise review and Why ML now
- Module 3: Graded Quiz
- Building and evaluating ML models
- Features and labels
- Building labeled datasets
- Training an ML model
- General best practices
- Identifying Damaged Car Parts with Vertex AutoML Vision
- Lab 1: Review
- Module 4: Graded Quiz
- Using ML responsibly and ethically
- Human bias in ML
- Google's AI Principles
- Common types of human bias
- Evaluating model fairness
- Guidelines and Hands-on Lab
- Inspecting a Dataset for Bias using TensorFlow Data Validation and Facets Overview
- Lab 2: Review
- Module 5: Graded Quiz
- Discovering ML use cases in day-to-day business
- Replacing rule-based systems with ML
- Automate processes and understand unstructured data
- Personalize applications with ML
- Creative uses of ML
- Sentiment analysis and Hands-on Lab
- Sentiment Analysis with Natural Language API
- Lab 3: Review
- Module 6: Graded Quiz
- Managing ML projects successfully
- Key consideration 1: business value
- Data strategy (pillars 1–3)
- Data strategy (pillars 4–7)
- Data governance
- Build successful ML teams
- Create a culture of innovation and Hands-on Lab
- Evaluate an ML model with BigQuery ML
- Lab 4: Review
- Module 7: Graded Quiz
- Summary
- Summary
- Course Resources
- Course Resources
- Your Next Steps
- Course Badge
