As a Google Cloud Partner, Jellyfish has been selected to deliver this course, which is aimed at business professionals in non-technical roles who are looking to lead or influence machine learning projects.
During the session, you’ll delve into machine learning, while bypassing all 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.
Our Managing Machine Learning Projects with Google Cloud course is available as a private training session that can be delivered via Virtual Classroom, at our training centre in The Shard, or at a location of your choice in the UK.
Who should attend:
This course is suitable for enterprise, corporate, or SMB business professionals in technical roles. You may be a business analyst, IT manager, project manager, or product manager. We recommend that VPs and above attend the Data-driven Transformation with Google Cloud course instead.
What you'll learn:
By the end of this course, you will be able to:
- Understand 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
- Recognise 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
No prior technical knowledge is required to attend this course, but you should be knowledgeable about your own business and objectives. We also recommend completing the Business Transformation with Google Cloud course beforehand.
- 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
- Differentiate between supervised and unsupervised machine learning problem types
- Identify examples of regression, classification, and clustering problem statements
- Recognise the core components of Google’s standard definition for ML and considerations for each when carrying out an ML project
- 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
- Discover common machine learning opportunities in day-to-day business processes
- Identify the requirements for businesses to successfully use ML
- Summarise key concepts and tools covered in the course content
- Compete for best ML use case presentation based on creativity, originality, and feasibility