Managing Machine Learning projects with Google Cloud (MMLPGC) - Outline

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.