Detailed Course Outline
Module 01: Introduction
- Differentiate between AI, machine learning, and deep learning.
- Describe the high-level uses of ML to improve business processes or to create new value.
- Begin assessing the feasibility of ML use cases.
Module 02: What is Machine Learning
- Differentiate between supervised and unsupervised machine learning problem types.
- Identify examples of regression, classification, and clustering problem statements.
- Recognize the core components of Google’s standard definition for ML and considerations for each when carrying out an ML project.
Module 03: Employing ML
- Describe the end-to-end process to carry out an ML project and considerations within each phase.
- Practice pitching a custom ML problem statement that has the potential to meaningfully impact your business.
Module 04: Discovering ML Use Cases
- Discover common machine learning opportunities in day-to-day business processes
Module 05: How to be Successful at ML
- Identify the requirement for businesses to successfully use ML
Module 06: Summary
- Summarize key concepts and tools covered in the course content.
- Compete for best ML use case presentation based on creativity, originality, and feasibility.