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.
Managing Machine Learning Projects with Google Cloud
- Intermedio
Pr贸ximos inicios
No disponibles en este momento.
Objetivos
- Thoroughly understand how ML can be used to improve business processes and create new value
- Thoroughly 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, model training, evaluation, and 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
Requisitos
- No prior technical knowledge is required
- No prior technical knowledge is required
- Savvy about your own business and objectives
- Recommended: Business Transformation with Google Cloud (on-demand)
Contenidos
Modulo 1: Introduction
- Differentiate between AI, machine learning, and deep learning
- 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
Modulo 2: What is Machine Learning?
- Differentiate between supervised and unsupervised machine learning problem types
- 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
Modulo 3: Employing ML
- Describe the end-to-end process to carry out an ML project and considerations within each phase
- 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
Modulo 4: Discovering ML Use Cases
- Discover common machine learning opportunities in day-to-day business processes
Modulo 5: How to Be Successful at ML
- Identify the requirements for businesses to successfully use ML
Modulo 6: Summary
- Summarize key concepts and tools covered in the course content
- Summarize key concepts and tools covered in the course content
- Compete for best ML use case presentation based on creativity, originality, and feasibility
Material del curso
Documentaci贸n Oficial de Google Cloud – Managing Machine Learning Projects with Google Cloud
Perfil del docente
- Formador Certificado por GCP
- M谩s de 5 a帽os de experiencia profesional
- M谩s de 4 a帽os de experiencia docente
- Profesional activo en empresas del sector IT
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