Managing Machine Learning Projects with Google Cloud
Business professionals in non-technical roles have a unique opportunity to lead and influence machine learning projects. In this course, you’ll explore machine learning without the technical jargon. You’ll learn how to translate business problems into custom machine learning use cases, assess each phase of the project, and translate the requirements to your technical team.
This course teaches participants the following skills:
- Gain a thorough understanding of how ML can be used to improve business processes and create new value.
- Explore common machine learning use cases implemented by businesses.
- Identify the requirements to carry out an ML project from assessing feasibility, to data preparation, to model training, to evaluation, to deployment.
- Define data characteristics and biases that affect the quality of ML models.
- Recognize key considerations for managing ML projects including data strategy, governance, and project teams.
- Pitch a custom ML use case that can meaningfully impact your business.
To get the most out of this course, participants should have:
- No prior technical knowledge is required.
- Savvy about your own business and objectives.
- Recommended: completing the Business Transformation with Google Cloud course.
Who Should Attend
- Enterprise, corporate, or SMB business professionals in non-technical roles.
- Roles include but are not limited to: business analysts, IT managers, project managers, product managers.
- For senior VPs and above, Data-Driven Transformation with Google Cloud is more suitable.
Google Cloud Platform Training Course Outline
Module 1: Introduction
- Overview: what is machine learning?
- Key terms: Artificial intelligence, machine learning, and deep learning
- Real-world examples of machine learning
- Overview: five phases in a machine learning project
- Phase 1: Assess the ML use case for specificity and difficulty
- Brainstorm a minimum of three custom ML use cases
Module 2: What is Machine Learning
- Common ML problem types
- Standard algorithms
- Data characteristics
- Predictive insights and decisions
- More real-life ML use cases
- Why ML now
Module 3: Employing ML
- Features and labels
- Building labeled data sets
- Training an ML model
- Evaluating an ML model
- General best practices
- Human bias and ML fairness
- Part 1: custom ML use case proposal
Module 4: Discovering ML Use Cases
- Replacing rules with machine learning
- Automating business processes with machine learning
- Understanding unstructured data with machine learning
- Personalizing applications with machine learning
- Creative use cases with machine learning
Module 5: How to Be Successful at ML
- Key considerations
- Formulating a data strategy
- Developing governance around uses of machine learning
- Building successful machine learning teams
- Creating a culture of innovation
Module 6: Summary
- Summary, presentations, feedback form