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Este é o curso Art and Science of Machine Learning. O curso tem seis módulos. Falaremos sobre as habilidades essenciais de intuição, bom senso e experimentação em ML para ajustar e otimizar modelos e ter melhor desempenho. Você aprenderá a generalizar os modelos usando técnicas de regularização e conhecerá os efeitos dos hiperparâmetros, como tamanho de lote e taxa de aprendizado, sobre o desempenho do modelo. Também abordaremos alguns algoritmos mais comuns de otimização de modelo e mostraremos como especificar um método de otimização no código do TensorFlow.
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What's inside
Syllabus
Introdução
Este é o curso Art and Science of Machine Learning. Falaremos sobre as habilidades essenciais de intuição, bom senso e experimentação em ML para ajustar e otimizar modelos e ter melhor desempenho. Você aprenderá a generalizar os modelos usando técnicas de regularização e conhecerá os efeitos dos hiperparâmetros, como tamanho de lote e taxa de aprendizado, sobre o desempenho do modelo. Também abordaremos alguns algoritmos mais comuns de otimização de modelo e mostraremos como especificar um método de otimização no código do TensorFlow.
Read more
Syllabus
Introdução
Este é o curso Art and Science of Machine Learning. Falaremos sobre as habilidades essenciais de intuição, bom senso e experimentação em ML para ajustar e otimizar modelos e ter melhor desempenho. Você aprenderá a generalizar os modelos usando técnicas de regularização e conhecerá os efeitos dos hiperparâmetros, como tamanho de lote e taxa de aprendizado, sobre o desempenho do modelo. Também abordaremos alguns algoritmos mais comuns de otimização de modelo e mostraremos como especificar um método de otimização no código do TensorFlow.
Read more
A arte do ML
Neste módulo, você aprenderá a ajustar o tamanho do lote e a taxa de aprendizado para melhorar o desempenho do modelo, otimizá-lo e aplicar os conceitos ao código do TensorFlow.
Ajuste de hiperparâmetros
Neste módulo, você aprenderá a diferenciar parâmetros e hiperparâmetros. Em seguida, veremos a abordagem tradicional de pesquisa de grade e outras com algoritmos mais inteligentes. Por fim, você verá como o Cloud ML Engine facilita a automação do ajuste de hiperparâmetros.
Uma pitada de ciência
Neste módulo, falaremos da ciência junto com a arte do machine learning. Primeiro vamos falar sobre como fazer a regularização da esparsidade e criar modelos mais simples e concisos. Depois abordaremos a regressão logística e veremos como determinar o desempenho.
A ciência das redes neurais
Neste módulo, vamos nos aprofundar na ciência, especificamente as redes neurais.
Embeddings
Neste módulo, você aprenderá a usar embeddings para gerenciar dados esparsos, acelerando o treinamento e reduzindo o consumo de memória dos modelos de machine learning que usam esses dados. Os embeddings também são uma forma de reduzir a dimensionalidade e tornar os modelos mais simples e generalizáveis.
Resumo
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Pode ajudar alunos iniciantes a aprimorar suas habilidades básicas de ML
Desenvolve habilidades essenciais em ML, como intuição, bom senso e experimentação
Ajuda alunos a otimizar e ajustar modelos de ML para melhorar o desempenho
Oferece uma introdução à teoria e técnicas comuns de otimização de modelos
Requer que os alunos tenham algum conhecimento prévio de ML
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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 Art and Science of Machine Learning em Português Brasileiro with these
activities:
Revise the fundamentals of machine learning
Show steps
Revising the fundamentals of machine learning will provide you with a strong foundation and make it easier to grasp the advanced concepts covered in the course.
Browse courses on
Machine Learning Basics
Show steps
-
Review your notes or textbooks on machine learning concepts such as supervised and unsupervised learning, feature engineering, and model evaluation.
-
Solve practice problems or take online quizzes to test your understanding of the core principles.
-
Attend a workshop or webinar on machine learning to refresh your knowledge and learn about the latest advancements in the field.
Connect with experienced machine learning professionals
Show steps
Mentorship provides guidance, support, and valuable insights.
Show steps
-
Identify potential mentors in your network or through online platforms.
-
Reach out to mentors and express your interest in learning from them.
-
Schedule regular meetings to discuss your progress and seek advice.
Participate in peer study groups
Show steps
Participating in peer study groups will provide you with opportunities to discuss course material, ask questions, and learn from your peers.
Show steps
-
Find a study group or create your own with classmates.
-
Meet regularly to discuss the course material, work on assignments, and prepare for exams.
-
Take turns leading discussions and presenting your understanding of different concepts.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Revise linear algebra
Show steps
Linear algebra is essential for understanding the mathematical foundations of machine learning algorithms.
Browse courses on
Matrix Operations
Show steps
-
Review the basics of matrix operations, such as addition, subtraction, and multiplication.
-
Understand the concept of vector spaces and subspaces.
-
Learn about linear transformations and their properties.
Practice tuning hyperparameters
Show steps
Practicing tuning hyperparameters will help you develop the skills necessary to optimize your machine learning models and improve their performance.
Browse courses on
Hyperparameter Tuning
Show steps
-
Experiment with different hyperparameter settings for a given machine learning algorithm.
-
Use a hyperparameter optimization library to automate the process and find the best settings for your model.
-
Compare the performance of your model before and after hyperparameter tuning to see the impact of your changes.
Solve machine learning practice problems
Show steps
Solving practice problems helps reinforce the concepts learned in the course and improve problem-solving skills.
Browse courses on
Supervised Learning
Show steps
-
Find practice problems online or in textbooks.
-
Start with easier problems and gradually move on to more challenging ones.
-
Check your solutions and identify areas for improvement.
Join a machine learning study group
Show steps
Participating in a study group provides opportunities for collaboration and knowledge sharing.
Show steps
-
Find a study group or create your own.
-
Meet regularly to discuss course material, work on projects, and share ideas.
-
Provide feedback and support to other group members.
Participate in machine learning competitions
Show steps
Participating in competitions helps you benchmark your skills and learn from others.
Show steps
-
Choose competitions that are relevant to your interests and skill level.
-
Form a team or work individually.
-
Develop and submit your solutions.
-
Analyze the results and identify areas for improvement.
Contribute to open-source machine learning projects
Show steps
Open-source contributions demonstrate your skills and help you connect with the machine learning community.
Show steps
-
Find open-source projects that align with your interests.
-
Start by contributing small fixes or improvements.
-
Gradually take on larger responsibilities and become a regular contributor.
Follow tutorials on advanced machine learning techniques
Show steps
Following tutorials provides hands-on experience with cutting-edge machine learning techniques.
Browse courses on
Natural Language Processing
Show steps
-
Identify areas where you want to deepen your knowledge.
-
Find reputable online tutorials or courses that cover those topics.
-
Follow the tutorials step-by-step and implement the techniques in your own projects.
Follow tutorials on advanced machine learning techniques
Show steps
Following tutorials on advanced machine learning techniques will expose you to new concepts and algorithms and help you expand your knowledge in the field.
Browse courses on
Deep Learning
Show steps
-
Identify specific advanced machine learning techniques that you are interested in learning.
-
Search for online tutorials or courses that cover these techniques.
-
Follow the tutorials step-by-step and implement the techniques in your own projects.
Build a machine learning portfolio
Show steps
Building a portfolio showcases your skills and knowledge, and helps you stand out in the job market.
Show steps
-
Choose projects that align with your interests and career goals.
-
Gather data and explore different machine learning algorithms.
-
Build and train models, and evaluate their performance.
-
Deploy your models and track their impact.
Contribute to open-source machine learning projects
Show steps
Contributing to open-source machine learning projects will give you hands-on experience, allow you to learn from others, and help advance the field.
Browse courses on
Community Involvement
Show steps
-
Identify open-source machine learning projects that align with your interests.
-
Start by contributing small changes or bug fixes.
-
Gradually take on larger tasks and responsibilities as you become more familiar with the project.
Build a machine learning project
Show steps
Building a machine learning project will allow you to apply the concepts learned in the course to a real-world problem and demonstrate your skills and understanding.
Show steps
-
Define the problem you want to solve and gather the necessary data.
-
Choose appropriate machine learning algorithms and train your models.
-
Evaluate the performance of your models and make improvements as needed.
-
Deploy your model and monitor its performance in a real-world setting.
Revise the fundamentals of machine learning
Show steps
Revising the fundamentals of machine learning will provide you with a strong foundation and make it easier to grasp the advanced concepts covered in the course.
Browse courses on
Machine Learning Basics
Show steps
Show steps
Show steps
- Review your notes or textbooks on machine learning concepts such as supervised and unsupervised learning, feature engineering, and model evaluation.
- Solve practice problems or take online quizzes to test your understanding of the core principles.
- Attend a workshop or webinar on machine learning to refresh your knowledge and learn about the latest advancements in the field.
Connect with experienced machine learning professionals
Show steps
Mentorship provides guidance, support, and valuable insights.
Show steps
Show steps
Show steps
- Identify potential mentors in your network or through online platforms.
- Reach out to mentors and express your interest in learning from them.
- Schedule regular meetings to discuss your progress and seek advice.
Participate in peer study groups
Show steps
Participating in peer study groups will provide you with opportunities to discuss course material, ask questions, and learn from your peers.
Show steps
Show steps
Show steps
- Find a study group or create your own with classmates.
- Meet regularly to discuss the course material, work on assignments, and prepare for exams.
- Take turns leading discussions and presenting your understanding of different concepts.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Revise linear algebra
Show steps
Linear algebra is essential for understanding the mathematical foundations of machine learning algorithms.
Browse courses on
Matrix Operations
Show steps
Show steps
Show steps
- Review the basics of matrix operations, such as addition, subtraction, and multiplication.
- Understand the concept of vector spaces and subspaces.
- Learn about linear transformations and their properties.
Practice tuning hyperparameters
Show steps
Practicing tuning hyperparameters will help you develop the skills necessary to optimize your machine learning models and improve their performance.
Browse courses on
Hyperparameter Tuning
Show steps
Show steps
Show steps
- Experiment with different hyperparameter settings for a given machine learning algorithm.
- Use a hyperparameter optimization library to automate the process and find the best settings for your model.
- Compare the performance of your model before and after hyperparameter tuning to see the impact of your changes.
Solve machine learning practice problems
Show steps
Solving practice problems helps reinforce the concepts learned in the course and improve problem-solving skills.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks.
- Start with easier problems and gradually move on to more challenging ones.
- Check your solutions and identify areas for improvement.
Join a machine learning study group
Show steps
Participating in a study group provides opportunities for collaboration and knowledge sharing.
Show steps
Show steps
Show steps
- Find a study group or create your own.
- Meet regularly to discuss course material, work on projects, and share ideas.
- Provide feedback and support to other group members.
Participate in machine learning competitions
Show steps
Participating in competitions helps you benchmark your skills and learn from others.
Show steps
Show steps
Show steps
- Choose competitions that are relevant to your interests and skill level.
- Form a team or work individually.
- Develop and submit your solutions.
- Analyze the results and identify areas for improvement.
Contribute to open-source machine learning projects
Show steps
Open-source contributions demonstrate your skills and help you connect with the machine learning community.
Show steps
Show steps
Show steps
- Find open-source projects that align with your interests.
- Start by contributing small fixes or improvements.
- Gradually take on larger responsibilities and become a regular contributor.
Follow tutorials on advanced machine learning techniques
Show steps
Following tutorials provides hands-on experience with cutting-edge machine learning techniques.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify areas where you want to deepen your knowledge.
- Find reputable online tutorials or courses that cover those topics.
- Follow the tutorials step-by-step and implement the techniques in your own projects.
Follow tutorials on advanced machine learning techniques
Show steps
Following tutorials on advanced machine learning techniques will expose you to new concepts and algorithms and help you expand your knowledge in the field.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify specific advanced machine learning techniques that you are interested in learning.
- Search for online tutorials or courses that cover these techniques.
- Follow the tutorials step-by-step and implement the techniques in your own projects.
Build a machine learning portfolio
Show steps
Building a portfolio showcases your skills and knowledge, and helps you stand out in the job market.
Show steps
Show steps
Show steps
- Choose projects that align with your interests and career goals.
- Gather data and explore different machine learning algorithms.
- Build and train models, and evaluate their performance.
- Deploy your models and track their impact.
Contribute to open-source machine learning projects
Show steps
Contributing to open-source machine learning projects will give you hands-on experience, allow you to learn from others, and help advance the field.
Browse courses on
Community Involvement
Show steps
Show steps
Show steps
- Identify open-source machine learning projects that align with your interests.
- Start by contributing small changes or bug fixes.
- Gradually take on larger tasks and responsibilities as you become more familiar with the project.
Build a machine learning project
Show steps
Building a machine learning project will allow you to apply the concepts learned in the course to a real-world problem and demonstrate your skills and understanding.
Show steps
Show steps
Show steps
- Define the problem you want to solve and gather the necessary data.
- Choose appropriate machine learning algorithms and train your models.
- Evaluate the performance of your models and make improvements as needed.
- Deploy your model and monitor its performance in a real-world setting.
Career center
Learners who complete Art and Science of Machine Learning em Português Brasileiro will develop knowledge and skills
that may be useful to these careers:
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. They work with a variety of data sources and technologies to push the boundaries of machine learning. This course may be useful for Machine Learning Researchers who want to improve their understanding of the art and science of machine learning. The course covers topics such as adjusting the size of batches and learning rates, optimizing models, and applying these concepts to TensorFlow code.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain artificial intelligence systems. They work with a variety of programming languages and technologies to create systems that can learn from data and make decisions. This course may be useful for Artificial Intelligence Engineers who want to improve their understanding of the art and science of machine learning. The course covers topics such as adjusting the size of batches and learning rates, optimizing models, and applying these concepts to TensorFlow code.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They develop and apply machine learning models to identify trading opportunities and manage risk. This course may be useful for Quantitative Analysts who want to improve their understanding of the art and science of machine learning. The course covers topics such as adjusting the size of batches and learning rates, optimizing models, and applying these concepts to TensorFlow code.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design, develop, and maintain computer vision systems. They work with a variety of image and video data to create systems that can recognize objects, track motion, and understand the content of images and videos. This course may be useful for Computer Vision Engineers who want to improve their understanding of the art and science of machine learning. The course covers topics such as adjusting the size of batches and learning rates, optimizing models, and applying these concepts to TensorFlow code.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers design, develop, and maintain natural language processing systems. They work with a variety of text and speech data to create systems that can understand human language. This course may be useful for Natural Language Processing Engineers who want to improve their understanding of the art and science of machine learning. The course covers topics such as adjusting the size of batches and learning rates, optimizing models, and applying these concepts to TensorFlow code.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers implement, maintain, and improve machine learning systems. They work closely with data scientists to bring machine learning models into production. This course may be useful for Machine Learning Engineers who want to improve their understanding of the art and science of machine learning. The course covers topics such as adjusting the size of batches and learning rates, optimizing models, and applying these concepts to TensorFlow code.
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to extract meaningful insights from data. They develop and apply machine learning models to solve business problems. This course may be useful for Data Scientists who want to improve their understanding of the art and science of machine learning. The course covers topics such as adjusting the size of batches and learning rates, optimizing models, and applying these concepts to TensorFlow code.
Business Analyst
Business Analyst
Business Analysts use data and analysis to help businesses make better decisions. They work with a variety of stakeholders to identify business problems and develop solutions. This course may be useful for Business Analysts who want to learn more about machine learning and how to apply it to their work. The course covers topics such as adjusting the size of batches and learning rates, optimizing models, and applying these concepts to TensorFlow code.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain 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 Software Engineers who want to learn more about machine learning and how to apply it to their work. The course covers topics such as adjusting the size of batches and learning rates, optimizing models, and applying these concepts to TensorFlow code.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. They work with a variety of data sources and technologies to analyze data and identify trends. This course may be useful for Data Analysts who want to learn more about machine learning and how to apply it to their work. The course covers topics such as adjusting the size of batches and learning rates, optimizing models, and applying these concepts to TensorFlow code.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to help businesses make better decisions. They work with a variety of data sources and technologies to analyze data and identify trends. This course may be useful for Business Intelligence Analysts who want to learn more about machine learning and how to apply it to their work. The course covers topics such as adjusting the size of batches and learning rates, optimizing models, and applying these concepts to TensorFlow code.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work with a variety of data sources and technologies to ensure that data is clean, consistent, and accessible. This course may be useful for Data Engineers who want to learn more about machine learning and how to apply it to their work. The course covers topics such as adjusting the size of batches and learning rates, optimizing models, and applying these concepts to TensorFlow code.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and maintain robots. They work with a variety of hardware and software to create robots that can perform a variety of tasks, such as walking, talking, and interacting with the environment. This course may be useful for Robotics Engineers who want to learn more about machine learning and how to apply it to their work. The course covers topics such as adjusting the size of batches and learning rates, optimizing models, and applying these concepts to TensorFlow code.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with a variety of stakeholders to define product requirements, prioritize features, and track progress. This course may be useful for Product Managers who want to learn more about machine learning and how to apply it to their work. The course covers topics such as adjusting the size of batches and learning rates, optimizing models, and applying these concepts to TensorFlow code.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They work with a variety of stakeholders to define project goals, develop project plans, and track progress. This course may be useful for Project Managers who want to learn more about machine learning and how to apply it to their work. The course covers topics such as adjusting the size of batches and learning rates, optimizing models, and applying these concepts to TensorFlow code.
For more career information including salaries, visit:
OpenCourser.com/course/1l6eq4/art
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Art and Science of Machine Learning em Português Brasileiro.
Save
Este livro abrangente é uma referência essencial para aprendizado profundo. Ele cobre os fundamentos teóricos e práticos do aprendizado profundo, incluindo redes neurais, convolução e processamento de linguagem natural.
Este livro ensina aprendizado profundo usando as bibliotecas TensorFlow 2 e Keras. Ele cobre tópicos como redes neurais convolucionais, redes neurais recorrentes e processamento de linguagem natural.
Este livro ensina aprendizado profundo usando a biblioteca Python Keras. Ele cobre tópicos como redes neurais convolucionais, redes neurais recorrentes e processamento de linguagem natural.
Este livro é uma introdução abrangente ao aprendizado por reforço, um tipo de aprendizado de máquina onde os agentes aprendem interagindo com seu ambiente.
Este livro prático guia os alunos através do processo de construção e implantação de modelos de aprendizado de máquina. Ele cobre tópicos essenciais, como seleção de recursos, ajuste de modelo e avaliação de desempenho.
Este livro prático guia os alunos através da construção e implantação de modelos de aprendizado de máquina usando Python. Ele cobre tópicos como pré-processamento de dados, seleção de recursos e avaliação de desempenho.
Este livro ensina aprendizado de máquina para desenvolvedores. Ele cobre tópicos como pré-processamento de dados, seleção de recursos e avaliação de desempenho.
Este livro ensina aprendizado de máquina usando a linguagem de programação R. Ele cobre tópicos como regressão, classificação e aprendizado não supervisionado.
Este livro fornece uma introdução abrangente ao aprendizado de máquina usando R. Ele cobre tópicos como regressão, classificação e aprendizado não supervisionado.
Este livro oferece uma visão geral abrangente do aprendizado de máquina. Ele cobre tópicos como regressão, classificação e aprendizado não supervisionado.
Este livro oferece uma introdução abrangente ao machine learning, cobrindo conceitos fundamentais, algoritmos e aplicações. É um ótimo recurso para iniciantes que desejam obter uma base sólida em ML.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1l6eq4/art
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Effort
Três semanas de estudo, cinco a sete horas por semana
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
Português
Transcript
English
This course belongs to a
collection
called
Machine Learning with TensorFlow on Google Cloud em Português Brasileiro. It's comprised of five courses:
- Intro to TensorFlow em Português Brasileiro
- Launching into Machine Learning em Português Brasileiro
- Feature Engineering em Português Brasileiro
- Art and Science of Machine Learning em Português Brasileiro (viewing)
- How Google does Machine Learning em Português Brasileiro
Good to know
Pode ajudar alunos iniciantes a aprimorar suas habilidades básicas de ML
Desenvolve habilidades essenciais em ML, como intuição, bom senso e experimentação
Ajuda alunos a otimizar e ajustar modelos de ML para melhorar o desempenho
Oferece uma introdução à teoria e técnicas comuns de otimização de modelos
Requer que os alunos tenham algum conhecimento prévio de ML
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