Trophy Arki1, Google Cloud Authorized Training Partner of the years 2019 & 2020 in Latin America

Managing Machine Learning Projects with Google Cloud

Profissionais de negócios em funções não técnicas têm uma oportunidade única de liderar e influenciar projetos de aprendizado de máquina. Neste curso, você explorará o aprendizado de máquina sem o jargão técnico. Você aprenderá a traduzir problemas de negócios em casos de uso de aprendizado de máquina personalizados, avaliar cada fase do projeto e traduzir os requisitos para sua equipe técnica.

Público-Alvo

Esta aula destina-se a:

  • 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.

Pré-requisitos

Para aproveitar ao máximo este curso, os participantes precisam atender aos seguintes critérios:
  • No prior technical knowledge is required.
  • Saavy about your own business and objectives.
  • Recommended: completing the Business Transformation with Google Cloud course.

Duração

~16 horas (2 dias)

Investimento

Caso tenha interesse em uma turma para sua empresa, por favor entre em contato conosco.

Resumo do curso

O curso inclui apresentações, demonstrações e atividades imersivas.
  • 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.
  • Common ML problem types.
  • Standard algorithms.
  • Data characteristics.
  • Predictive insights and decisions.
  • More real-life ML use cases.
  • Why ML now.
  • 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.
  • 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.
  • Key considerations.
  • Formulating a data strategy.
  • Developing governance around uses of machine learning.
  • Building successful machine learning teams.
  • Creating a culture of innovation.
  • Summary, presentations, feedback form.