TensorFlow on Google Cloud - Português Brasileiro from Coursera

What's inside

Syllabus

Introdução ao curso

Este módulo apresenta uma visão geral do curso e dos objetivos a serem alcançados.

Introdução ao ecossistema do TensorFlow

Este módulo apresenta o framework do TensorFlow e dá uma prévia dos principais componentes da plataforma, bem como a hierarquia geral da API.

Design e criação de pipeline de dados de entrada

Os dados são um componente crucial de um modelo de machine learning. Coletar os certos não é suficiente. Você também precisa verificar se os processos certos estão em andamento para limpar, analisar e transformar os dados de acordo com a necessidade, e para que o modelo possa extrair a maior quantidade de indicadores possível. Neste módulo, falamos sobre como treinar em grandes conjuntos de dados com a tf.data, trabalhar com os arquivos na memória e preparar os dados para treinamento. Falamos também sobre embeddings e finalizamos com uma visão geral do dimensionamento de dados com as camadas de pré-processamento da tf.keras.

Como criar redes neurais com o TensorFlow e a API Keras

Neste módulo, vamos falar sobre as funções de ativação e como elas são necessárias para que as redes neurais profundas capturem a não linearidade dos dados. Em seguida apresentamos uma visão geral das redes neurais profundas usando as APIs Keras Sequential e Functional. Também descrevemos a criação de subclasses de modelos, que oferece maior flexibilidade na hora de construir um deles. O módulo termina com uma aula sobre regularização.

Como treinar em escala com a Vertex AI

Neste módulo, vamos descrever como treinar os modelos do TensorFlow em escala com a Vertex AI.

Resumo

Neste módulo, você vai ver um resumo do TensorFlow no curso do Google Cloud.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers designing and implementing an ingestion data pipeline with TensorFlow 2.x

Helps learners develop ML models with TensorFlow 2.x and Keras

Provides guidance on how to create production-ready ML models

Covers how to develop specialized ML models

Taught by Google's cloud experts

Requires learners to come in with some experience using TensorFlow

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 TensorFlow on Google Cloud - Português Brasileiro with these activities:

Review Python

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Refreshes foundational Python knowledge to support success in this course

Browse courses on Python

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Review documentation
Solve practice problems
Build a simple program

Review Machine Learning Concepts

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Refreshes foundational machine learning concepts to support success in this course

Browse courses on Machine Learning

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Review documentation
Solve practice problems
Build a simple machine learning model

Solve TensorFlow Practice Problems

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Strengthens problem-solving skills specific to TensorFlow

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Find practice problems
Solve problems
Review solutions

Two other activities

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Follow TensorFlow Tutorials

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Provides additional guidance and reinforcement of TensorFlow concepts

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Find relevant tutorials
Follow the tutorials
Apply what you learn

Build a TensorFlow Project

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Applies TensorFlow concepts to a real-world problem, fostering deeper understanding

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Identify a project idea
Gather data
Build and train a model
Deploy the project

Career center

Learners who complete TensorFlow on Google Cloud - Português Brasileiro will develop knowledge and skills that may be useful to these careers:

Software Engineer

Software Engineers design, develop, and maintain software systems. They use their knowledge of programming languages, data structures, and algorithms to create software that meets the needs of users. This course may be useful for aspiring Software Engineers with an interest in machine learning, as it will give them hands-on experience in building and deploying machine learning models. Machine learning is an increasingly important part of modern software systems, and this course will give students the skills needed to work with it effectively.

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Data Engineer

Data Engineers design, build, and maintain data pipelines. They work with data scientists and other stakeholders to ensure that data is clean, accurate, and accessible. This course may be useful for aspiring Data Engineers because it will teach the fundamentals of machine learning, as well as how to apply these techniques to data engineering. The course covers topics such as data pipelines, data quality, and data security.

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Machine Learning Engineer

Machine Learning Engineers are experts in building and implementing machine learning models to solve real-world problems. Their responsibilities include designing, developing, and deploying machine learning systems, as well as monitoring and maintaining these systems over time. This course may be useful for someone seeking to become a Machine Learning Engineer because it will teach the fundamentals of machine learning and give practical experience in building machine learning models. The course covers topics such as data preprocessing, model training, and model evaluation.

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Financial Analyst

Financial Analysts use financial data to make investment recommendations. They analyze financial statements, conduct industry research, and build financial models to help investors make informed decisions. This course may be useful for aspiring Financial Analysts because it will teach the fundamentals of machine learning, as well as how to apply these techniques to financial data. The course covers topics such as financial modeling, portfolio management, and risk assessment.

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Quantitative Analyst

Quantitative Analysts (Quants) use mathematical and statistical models to analyze financial data and make investment decisions. They are responsible for developing and implementing trading strategies, as well as managing risk. This course may be useful for aspiring Quants because it will teach the fundamentals of machine learning, as well as how to apply these techniques to financial data. The course covers topics such as time series analysis, risk management, and portfolio optimization.

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Risk Manager

Risk Managers identify, assess, and manage risks. They work with organizations to develop and implement strategies to mitigate risks and protect assets. This course may be useful for aspiring Risk Managers because it will teach the fundamentals of machine learning, as well as how to apply these techniques to risk management. The course covers topics such as risk identification, risk assessment, and risk mitigation.

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Data Scientist

Data Scientists use machine learning to translate raw data into valuable business decisions. Their duties commonly include gathering and analyzing data, building machine learning models, and presenting insights to stakeholders. This course may be useful for an aspiring Data Scientist because it will teach the fundamentals of building and applying machine learning models. While this course is not specific to data science, it will teach techniques that are commonly used by data scientists.

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Market Research Analyst

Market Research Analysts collect, analyze, and interpret market data to help organizations make informed decisions. They use statistical and machine learning techniques to identify trends and patterns in data, and they communicate their findings to stakeholders in a clear and concise way. This course may be useful for someone seeking to become a Market Research Analyst because it will teach the fundamentals of machine learning, as well as how to gather and analyze data. The course also covers topics such as data visualization and communication.

See salaries and explore the career path for Market Research Analyst

Data Analyst

Data Analysts collect, analyze, and interpret data to help organizations make informed decisions. They use statistical and machine learning techniques to identify trends and patterns in data, and they communicate their findings to stakeholders in a clear and concise way. This course may be useful for someone seeking to become a Data Analyst because it will teach the fundamentals of machine learning, as well as how to preprocess and analyze data. The course also covers topics such as data visualization and communication, which are important skills for Data Analysts.

See salaries and explore the career path for Data Analyst

Solutions Architect

Solutions Architects design and implement technology solutions for organizations. They work with clients to understand their business needs and develop solutions that meet those needs. This course may be useful for aspiring Solutions Architects because it will teach the fundamentals of machine learning, as well as how to apply these techniques to solution architecture. The course covers topics such as cloud computing, big data, and artificial intelligence.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve complex business problems. They work with organizations to improve efficiency, productivity, and profitability. This course may be useful for aspiring Operations Research Analysts because it will teach the fundamentals of machine learning, as well as how to apply these techniques to operations research problems. The course covers topics such as linear programming, optimization, and simulation.

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Business Analyst

Business Analysts use data to help organizations improve their performance. They work with stakeholders to identify business needs, gather and analyze data, and make recommendations for improvement. This course may be useful for aspiring Business Analysts because it will teach the fundamentals of machine learning, as well as how to apply these techniques to business problems. The course covers topics such as data mining, predictive analytics, and decision making.

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DevOps Engineer

DevOps Engineers work to bridge the gap between development and operations teams. They are responsible for building, deploying, and maintaining software systems. This course may be useful for aspiring DevOps Engineers because it will teach the fundamentals of machine learning, as well as how to apply these techniques to DevOps. The course covers topics such as continuous integration, continuous delivery, and infrastructure automation.

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Product Manager

Product Managers are 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 because it will teach the fundamentals of machine learning, as well as how to apply these techniques to product development. The course covers topics such as user research, market analysis, and product planning.

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Project Manager

Project Managers are responsible for planning, executing, and closing projects. They work with stakeholders to define project scope, develop project plans, and manage project risks. This course may be useful for aspiring Project Managers because it will teach the fundamentals of machine learning, as well as how to apply these techniques to project management. The course covers topics such as project planning, risk management, and stakeholder management.

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TensorFlow on Google Cloud - Português Brasileiro

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