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.
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