Managing Machine Learning Projects with Google Cloud

The "Introduction" module provides an overview of the course, including guidance on downloading course resources and sending feedback.

Module 2, "Identifying business value for using ML," introduces the distinction between AI, ML, and deep learning, and guides you through assessing the feasibility and potential impact of ML use cases.

In Module 3, "Defining ML as a practice," you will explore common ML problem types, data quality, predictive insights, and decisions, and engage in practical exercises to analyze ML use cases.

Module 4, "Building and evaluating ML models," focuses on understanding features and labels, building labeled datasets, training ML models, and best practices, culminating in hands-on labs to reinforce learning.

"Using ML responsibly and ethically" in Module 5 delves into human bias in ML, Google's AI Principles, evaluating model fairness, and inspecting datasets for bias using TensorFlow Data Validation and Facets.

In Module 6, "Discovering ML use cases in day-to-day business," you will explore replacing rule-based systems, automating processes, personalizing applications, and engaging in sentiment analysis through hands-on labs.

Finally, Module 7, "Managing ML projects successfully," covers key considerations for business value, data strategy, data governance, building successful ML teams, and fostering a culture of innovation through hands-on labs.

The closing Module 8, "Summary," provides a comprehensive overview of the course, consolidating your learnings from the preceding modules.