Quer saber mais sobre o Feature Store da Vertex AI, como melhorar a acurácia dos seus modelos de machine learning e quais colunas de dados contêm os atributos mais úteis? No curso "Feature Engineering", falamos sobre atributos bons e ruins, além de mostrar como fazer o pré-processamento e a transformação deles para otimizar seus modelos. Esse curso tem conteúdo teórico e prático sobre a engenharia de atributos usando o BigQuery ML, Keras e TensorFlow.
This course is part of the Machine Learning with TensorFlow on Google Cloud em Português Brasileiro Specialization
Feature Engineering em Português Brasileiro
About this Course
What you will learn
Usar o Feature Store da Vertex AI
Descrever a migração de dados brutos para atributos e executar a engenharia de atributos
Pré-processar atributos usando o Apache Beam e o Cloud DataFlow
Usar o tf.Transform
Skills you will gain
- Values Modes
- A Priori And A Posteriori
- Critical Value
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Syllabus - What you will learn from this course
Introdução ao curso
Neste módulo, apresentamos uma visão geral do curso e dos objetivos a serem alcançados.
Introdução ao Feature Store da Vertex AI
Este módulo apresenta o Feature Store da Vertex AI.
De dados brutos a atributos
A engenharia de atributos costuma ser a fase mais demorada e difícil da criação dos projetos de ML. Esse processo começa pelos dados brutos, e você usa seu próprio conhecimento sobre domínios para criar atributos que vão fazer seus algoritmos de machine learning funcionarem. Neste módulo, vamos conferir o que caracteriza um bom atributo e como fazer a representação deles no seu modelo.
Engenharia de atributos
Neste módulo, analisamos as diferenças entre o machine learning e as estatísticas, além de mostrar como executar a engenharia de atributos no BigQuery ML e no Keras. Também vamos abordar algumas práticas avançadas desse processo.
Pré-processamento e criação de atributos
Neste módulo, apresentamos informações sobre o Dataflow, uma tecnologia complementar do Apache Beam. Ambos podem ajudar você a criar e executar o pré-processamento e a engenharia de atributos.
Cruzamentos de atributos – TensorFlow Playground
Os cruzamentos de atributos não têm um papel muito significativo nos processos tradicionais de machine learning. Porém, para os métodos de ML atuais, esses tipos de recursos são parte essencial do seu kit de ferramentas. Neste módulo, você vai aprender a reconhecer os tipos de problemas em que os cruzamentos de atributos desempenham um papel importante no machine learning.
Introdução ao TensorFlow Transform
O TensorFlow Transform (tf.Transform) é uma biblioteca de pré-processamento de dados com o TensorFlow. O tf.Transform é útil para os pré-processamentos que exigem uma passagem completa dos dados, como:
• normalizar um valor de entrada por média e stdev; • transformar strings em números inteiros ao gerar um vocabulário com a verificação de todos os exemplos de entrada dos valores; • separar as entradas em buckets com base na distribuição de dados que foi observada. Neste módulo vamos apresentar os casos de uso do tf.Transform.
Este módulo é um resumo do curso Feature Engineering
About the Machine Learning with TensorFlow on Google Cloud em Português Brasileiro Specialization
O que é aprendizado de máquina e que tipos de problema ele pode resolver? Quais são as cinco fases da conversão de um possível caso de uso de aprendizado de máquina e por que é importante que elas não sejam ignoradas? Por que as redes neurais são tão requisitadas hoje? Como configurar um problema de aprendizado supervisionado, além de encontrar uma solução ótima e generalizável com gradiente descendente e uma boa forma de criar conjuntos de dados? Aprenda a gravar modelos de aprendizado de máquina distribuídos com escalonamento no TensorFlow, faça escalonamento horizontal do treinamento desses modelos e ofereça previsões de alto desempenho. Converta dados brutos em atributos para informar características importantes desses dados ao aprendizado de máquina e ofereça uma percepção humana para dar suporte ao problema. Por fim, aprenda a incorporar a combinação ideal de parâmetros que produz modelos precisos e generalizados, além de conhecer a teoria para resolver tipos específicos de problemas de aprendizado de máquina. Você passará por todas as etapas do aprendizado de máquina, desde a criação de uma estratégia voltada para aprendizado de máquina até o treinamento, a otimização e a produção de modelos em laboratórios práticos com o Google Cloud Platform.
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Frequently Asked Questions
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Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
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Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
When will I receive my Course Certificate?
If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.
Why can’t I audit this course?
This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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