Managing Machine Learning Projects with Google Cloud

• 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

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

• Formador Certificado por GCP
• Más de 5 años de experiencia profesional
• Más de 4 años de experiencia docente
• Profesional activo en empresas del sector IT