Serverless Machine Learning with Tensorflow on Google Cloud em Português Brasileiro from Coursera

Este curso intensivo sob demanda de quatro dias oferece aos participantes uma introdução sobre como projetar e criar sistemas de machine learning no Google Cloud Platform. Por meio de apresentações, demonstrações e laboratórios práticos, os participantes aprenderão os conceitos de machine learning (ML) e do TensorFlow, além de habilidades de desenvolvimento, avaliação e produção de modelos de ML.

OBJETIVOS

Neste curso, os participantes aprenderão as seguintes habilidades:

● Identificar casos de uso de machine learning

● Criar um modelo de ML usando o TensorFlow

● Criar modelos de ML escalonáveis e implantáveis usando o Cloud ML

● Saber a importância do pré-processamento e da combinação de atributos

● Incorporar conceitos avançados de ML aos modelos

● Produzir modelos de ML treinados

PRÉ-REQUISITOS

Para aproveitar ao máximo este curso, os participantes precisam cumprir os seguintes requisitos:

● Ter concluído o curso Google Cloud Platform Big Data and Machine Learning Fundamentals OU experiência equivalente

● Proficiência básica em linguagem de consulta comum, como SQL

● Experiência com atividades de modelagem, extração, transformação e carregamento de dados

● Desenvolvimento de aplicativos usando uma linguagem de programação comum, como Python

● Conhecimento de machine learning e/ou estatísticas

Notas da Conta do Google:

• No momento, os serviços do Google não estão disponíveis na China.

What's inside

Syllabus

Este é o "Serverless Machine Learning on Google Cloud Platform"

Módulo 1: Primeiros passos com machine learning

Módulo 2: Criação de modelos de ML com o TensorFlow

Módulo 3: Escalonamento de modelos de ML com o Cloud ML Engine

Módulo 4: Engenharia de atributos

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Ensina habilidades de desenvolvimento de machine learning escalonável e implantável

Oferece laboratórios práticos para reforçar a aprendizagem

Prepara os alunos para cenários de produção de machine learning

Reforça o pré-processamento e a combinação de atributos, aspectos cruciais do machine learning

Incorpora conceitos avançados de machine learning, expandindo o conhecimento dos alunos

Requer proficiência em SQL, modelagem de dados, extração, transformação e carregamento de dados

Reviews summary

Tensorflow machine learning mastery

This hands-on machine learning course from Google Cloud is designed for learners with some machine learning and programming experience. It provides a comprehensive introduction to machine learning concepts, TensorFlow, and model development, evaluation, and deployment on Google Cloud Platform. While some learners have experienced errors in the labs, overall, the course is highly rated for its clear presentations, demonstrations, and practical exercises.

Concepts and Skills

"Neste curso, os participantes aprenderão as seguintes habilidades:"

Exercises and Concepts

"Além de habilidades de desenvolvimento, avaliação e produção de modelos ..."

Some Errors

"Diversos labs estão com erro de execução."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Serverless Machine Learning with Tensorflow on Google Cloud em Português Brasileiro with these activities:

Review Machine Learning Fundamentals

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Ensure a strong foundational understanding of fundamental machine learning principles and concepts.

Browse courses on Machine Learning Fundamentals

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Revisit key concepts like supervised and unsupervised learning, model evaluation, and feature engineering.
Review materials from previous courses or online resources.

Practice Machine Learning Algorithms

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Develop proficiency in applying machine learning algorithms using TensorFlow.

Browse courses on Machine Learning Algorithms

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Work through coding exercises and projects using TensorFlow to implement various algorithms.
Participate in online forums or communities to discuss and troubleshoot algorithm implementations.

Develop a Machine Learning Project

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Apply course concepts to real-world scenarios by building a complete machine learning project.

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Define a problem statement and gather relevant data.
Preprocess and explore the data, selecting appropriate features.
Train and evaluate machine learning models using TensorFlow.
Deploy and monitor the model for practical use.

Show all three activities

Career center

Learners who complete Serverless Machine Learning with Tensorflow on Google Cloud em Português Brasileiro will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer builds and maintains machine learning models. They work closely with data scientists and software engineers to ensure that models are accurate, efficient, and scalable. This course may be useful for aspiring Machine Learning Engineers, as it provides a foundation in machine learning concepts and TensorFlow, a popular open-source machine learning library. The course also covers topics such as model scaling and deployment, which are essential for real-world machine learning applications.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist uses data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. They then use this information to develop predictive models and make recommendations. This course may be useful for aspiring Data Scientists, as it provides a foundation in machine learning concepts and TensorFlow. The course also covers topics such as data preprocessing and feature engineering, which are essential for building accurate machine learning models.

See salaries and explore the career path for Data Scientist

Software Engineer

A Software Engineer designs, develops, and maintains software systems. They work with a variety of programming languages and technologies to create software that meets the needs of users. This course may be useful for aspiring Software Engineers who want to learn more about machine learning. The course covers topics such as model deployment and scaling, which are essential for building production-ready machine learning applications.

See salaries and explore the career path for Software Engineer

Product Manager

A Product Manager is responsible for the development and launch of new products. They work with engineers, designers, and marketers to ensure that products meet the needs of users. This course may be useful for aspiring Product Managers who want to learn more about machine learning. The course covers topics such as model evaluation and selection, which are essential for building successful machine learning products.

See salaries and explore the career path for Product Manager

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical models to analyze financial data. They use this information to make investment decisions and develop trading strategies. This course may be useful for aspiring Quantitative Analysts who want to learn more about machine learning. The course covers topics such as model training and optimization, which are essential for building accurate machine learning models.

See salaries and explore the career path for Quantitative Analyst

Data Analyst

A Data Analyst collects, cleans, and analyzes data to identify trends and patterns. They use this information to make recommendations and improve business processes. This course may be useful for aspiring Data Analysts who want to learn more about machine learning. The course covers topics such as data visualization and exploratory data analysis, which are essential for understanding data and identifying opportunities for improvement.

See salaries and explore the career path for Data Analyst

Business Analyst

A Business Analyst works with businesses to identify and solve problems. They use data analysis and problem-solving skills to develop solutions that improve business outcomes. This course may be useful for aspiring Business Analysts who want to learn more about machine learning. The course covers topics such as data mining and predictive modeling, which are essential for building machine learning solutions that solve business problems.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

An Operations Research Analyst uses mathematical and statistical models to solve complex business problems. They work with businesses to improve efficiency, productivity, and decision-making. This course may be useful for aspiring Operations Research Analysts who want to learn more about machine learning. The course covers topics such as optimization and simulation, which are essential for building machine learning models that solve complex business problems.

See salaries and explore the career path for Operations Research Analyst

Market Researcher

A Market Researcher studies consumer behavior and market trends. They use this information to help businesses develop and market new products and services. This course may be useful for aspiring Market Researchers who want to learn more about machine learning. The course covers topics such as data collection and analysis, which are essential for understanding consumer behavior and market trends.

See salaries and explore the career path for Market Researcher

Financial Analyst

A Financial Analyst analyzes financial data to make investment decisions and develop financial plans. This course may be useful for aspiring Financial Analysts who want to learn more about machine learning. The course covers topics such as time series analysis and forecasting, which are essential for building machine learning models that can predict future financial trends.

See salaries and explore the career path for Financial Analyst

Healthcare Analyst

A Healthcare Analyst uses data to improve the quality and efficiency of healthcare delivery. They work with healthcare providers to identify and solve problems, and to develop new ways to improve patient care. This course may be useful for aspiring Healthcare Analysts who want to learn more about machine learning. The course covers topics such as medical data analysis and predictive modeling, which are essential for building machine learning models that can improve healthcare outcomes.

See salaries and explore the career path for Healthcare Analyst

Risk Analyst

A Risk Analyst identifies and assesses risks to businesses and organizations. They develop and implement strategies to mitigate risks and protect against losses. This course may be useful for aspiring Risk Analysts who want to learn more about machine learning. The course covers topics such as risk modeling and prediction, which are essential for building machine learning models that can identify and assess risks.

See salaries and explore the career path for Risk Analyst

Actuary

An Actuary uses mathematical and statistical models to assess and manage risks in the insurance and financial industries. This course may be useful for aspiring Actuaries who want to learn more about machine learning. The course covers topics such as statistical modeling and forecasting, which are essential for building machine learning models that can assess and manage risks.

See salaries and explore the career path for Actuary

Statistician

A Statistician collects, analyzes, and interprets data. They use this information to make recommendations and improve decision-making. This course may be useful for aspiring Statisticians who want to learn more about machine learning. The course covers topics such as data visualization and exploratory data analysis, which are essential for understanding data and identifying opportunities for improvement.

See salaries and explore the career path for Statistician

Data Engineer

A Data Engineer designs, builds, and maintains data pipelines. They work with data scientists and software engineers to ensure that data is available in a timely and reliable manner. This course may be useful for aspiring Data Engineers who want to learn more about machine learning. The course covers topics such as data cleaning and transformation, which are essential for building machine learning models that are accurate and efficient.

See salaries and explore the career path for Data Engineer