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Leire Ahedo
Este proyecto es un curso práctico y efectivo para aprender todo lo que necesitas saber acerca de autoSklearn. En este curso aprenderemos acerca de las librerías de autoML de autoSklearn y PipelineProfiler. También entrenaremos modelos para la predicción del...
Read more
Este proyecto es un curso práctico y efectivo para aprender todo lo que necesitas saber acerca de autoSklearn. En este curso aprenderemos acerca de las librerías de autoML de autoSklearn y PipelineProfiler. También entrenaremos modelos para la predicción del...
Read more
Este proyecto es un curso práctico y efectivo para aprender todo lo que necesitas saber acerca de autoSklearn. En este curso aprenderemos acerca de las librerías de autoML de autoSklearn y PipelineProfiler. También entrenaremos modelos para la predicción del cáncer, coste de vivienda, etc.
Register for this course and see more details by visiting:
OpenCourser.com/course/izzz7n/automl
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Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Helps learners apply their knowledge outside of a purely theoretical context
Provides a cost-efficient way to develop practical skills
Provides hands-on experience with autoML
Combines theoretical and practical knowledge
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Reviews summary
Automl with autosklearn and google colab
This course on AutoML with AutoSklearn and Google Colab is a great option for those looking to learn about autoML and its applications in practice. The course is well-received by students, with reviewers praising the project-based approach and the clear explanations provided by the instructor. However, some students have experienced technical difficulties with the installation process, which is worth considering before enrolling.
Course offers hands-on projects.
"Este proyecto es un curso práctico y efectivo para aprender todo lo que necesitas saber acerca de autoSklearn."
Some students encountered issues with installation.
"Instalar el AutoSklearn en Colab es toda una proeza y es lo que más consume tiempo, se debiera explicar un paso a paso de la instalación y mostrar todos los errores que pueden ocurrir, en esto es que se va todo el curso para el estudiante."
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 AutoML con AutoSklearn y Google Colab with these
activities:
Review autoML basics
Show steps
Review the fundamentals of autoML to strengthen understanding before the course begins.
Show steps
-
Read introductory materials on autoML concepts.
-
Complete online tutorials on autoML basics.
-
Review previous notes or coursework on machine learning.
Explore autoSklearn tutorials
Show steps
Follow guided tutorials to gain hands-on experience with autoSklearn.
Show steps
-
Find online tutorials on autoSklearn.
-
Step through the tutorials, following instructions carefully.
-
Experiment with different autoSklearn features and parameters.
Complete autoSklearn exercises
Show steps
Reinforce understanding by completing exercises and drills on autoSklearn.
Show steps
-
Find online exercises or practice problems on autoSklearn.
-
Solve the exercises, applying autoSklearn concepts.
-
Review solutions and identify areas for improvement.
Three other activities
Expand to see all activities and additional details
Show all six activities
Mentor fellow autoSklearn learners
Show steps
Reinforce understanding by helping others learn autoSklearn.
Show steps
-
Identify opportunities to mentor or tutor others in autoSklearn.
-
Provide guidance, support, and answer questions.
-
Reflect on and improve own understanding through the mentoring process.
Develop a case study using autoSklearn
Show steps
Solidify learning by applying autoSklearn to a real-world problem.
Show steps
-
Identify a suitable problem or dataset.
-
Apply autoSklearn to build and evaluate models.
-
Create a presentation or report outlining the process and results.
Contribute to autoSklearn community
Show steps
Expand knowledge and engage with the autoSklearn community.
Show steps
-
Find opportunities to contribute to autoSklearn's GitHub repository.
-
Report bugs, suggest improvements, or contribute code.
-
Participate in community discussions and forums.
Review autoML basics
Show steps
Review the fundamentals of autoML to strengthen understanding before the course begins.
Show steps
Show steps
Show steps
- Read introductory materials on autoML concepts.
- Complete online tutorials on autoML basics.
- Review previous notes or coursework on machine learning.
Explore autoSklearn tutorials
Show steps
Follow guided tutorials to gain hands-on experience with autoSklearn.
Show steps
Show steps
Show steps
- Find online tutorials on autoSklearn.
- Step through the tutorials, following instructions carefully.
- Experiment with different autoSklearn features and parameters.
Complete autoSklearn exercises
Show steps
Reinforce understanding by completing exercises and drills on autoSklearn.
Show steps
Show steps
Show steps
- Find online exercises or practice problems on autoSklearn.
- Solve the exercises, applying autoSklearn concepts.
- Review solutions and identify areas for improvement.
Three other activities
Expand to see all activities and additional details
Show all six activities
Mentor fellow autoSklearn learners
Show steps
Reinforce understanding by helping others learn autoSklearn.
Show steps
Show steps
Show steps
- Identify opportunities to mentor or tutor others in autoSklearn.
- Provide guidance, support, and answer questions.
- Reflect on and improve own understanding through the mentoring process.
Develop a case study using autoSklearn
Show steps
Solidify learning by applying autoSklearn to a real-world problem.
Show steps
Show steps
Show steps
- Identify a suitable problem or dataset.
- Apply autoSklearn to build and evaluate models.
- Create a presentation or report outlining the process and results.
Contribute to autoSklearn community
Show steps
Expand knowledge and engage with the autoSklearn community.
Show steps
Show steps
Show steps
- Find opportunities to contribute to autoSklearn's GitHub repository.
- Report bugs, suggest improvements, or contribute code.
- Participate in community discussions and forums.
Career center
Learners who complete AutoML con AutoSklearn y Google Colab will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
The AutoML con AutoSklearn y Google Colab course provides a solid foundation for aspiring Machine Learning Engineers by teaching them how to utilize cutting-edge autoML libraries for effective machine learning model building. The hands-on training in model building for real-world scenarios prepares learners for the challenges they will encounter in this role.
Data Scientist
Data Scientist
For individuals seeking a career as a Data Scientist, the AutoML con AutoSklearn y Google Colab course offers valuable insights into leveraging automated machine learning techniques for data analysis and modeling. With the advent of big data, Data Scientists are in high demand, and this course helps learners stay ahead of the curve in this field.
Data Analyst
Data Analyst
The AutoML con AutoSklearn y Google Colab course provides Data Analysts with a comprehensive understanding of autoML techniques, enabling them to automate data analysis and model building processes. By leveraging these advanced methods, Data Analysts can enhance their efficiency and contribute to more accurate data-driven decision-making.
Software Engineer
Software Engineer
The AutoML con AutoSklearn y Google Colab course can enhance the skills of Software Engineers seeking to specialize in machine learning and artificial intelligence. The course's emphasis on autoML libraries aligns with the growing demand for software engineers with expertise in developing ML-driven applications.
Quantitative Analyst
Quantitative Analyst
The AutoML con AutoSklearn y Google Colab course can benefit Quantitative Analysts by providing them with an understanding of autoML techniques for financial data analysis and modeling. As autoML becomes more prevalent in the financial industry, Quantitative Analysts with knowledge in this area will be well-positioned to contribute to improved risk assessment and investment strategies.
Business Analyst
Business Analyst
For individuals aspiring to become Business Analysts, the AutoML con AutoSklearn y Google Colab course offers practical knowledge in using autoML for business intelligence and predictive analytics. By understanding how to leverage automated machine learning, Business Analysts can gain insights from data more efficiently, supporting informed decision-making and strategic planning.
Statistician
Statistician
Statisticians who take the AutoML con AutoSklearn y Google Colab course will gain a deeper understanding of autoML techniques for data analysis and modeling. The course's focus on real-world applications provides Statisticians with practical experience in using autoML to solve complex statistical problems, enhancing their ability to contribute to scientific research and decision-making.
Actuary
Actuary
The AutoML con AutoSklearn y Google Colab course offers valuable knowledge for Actuaries seeking to incorporate autoML into their practice. By learning how to leverage automated machine learning techniques, Actuaries can improve the accuracy and efficiency of their risk assessments and financial modeling, leading to more informed decision-making.
Research Scientist
Research Scientist
The AutoML con AutoSklearn y Google Colab course can benefit Research Scientists involved in data analysis and modeling. By understanding how to leverage autoML techniques, Research Scientists can streamline their research processes, increase the efficiency of data analysis, and gain deeper insights into their research domains.
Product Manager
Product Manager
For Product Managers seeking to incorporate machine learning into their products, the AutoML con AutoSklearn y Google Colab course offers a valuable foundation. By understanding the capabilities and limitations of autoML, Product Managers can make informed decisions about integrating these techniques into their products, leading to more innovative and user-friendly applications.
Data Engineer
Data Engineer
The AutoML con AutoSklearn y Google Colab course provides Data Engineers with an overview of autoML techniques that can enhance their data engineering practices. By gaining knowledge in autoML, Data Engineers can develop more efficient and scalable data pipelines for machine learning models, supporting the growing demand for data-driven solutions.
Consultant
Consultant
The AutoML con AutoSklearn y Google Colab course can benefit Consultants specializing in data analysis and machine learning. By gaining knowledge in autoML techniques, Consultants can offer their clients more advanced and efficient solutions, enhancing their value proposition and expanding their service offerings.
Project Manager
Project Manager
Project Managers working on machine learning projects may find the AutoML con AutoSklearn y Google Colab course helpful in understanding the technical aspects of autoML. By gaining a basic understanding of autoML techniques and their applications, Project Managers can better manage and coordinate machine learning projects, ensuring their successful implementation.
Teacher
Teacher
Teachers in computer science or data science may find the AutoML con AutoSklearn y Google Colab course helpful for incorporating autoML into their curriculum. By learning about the principles and applications of autoML, Teachers can equip their students with the latest advancements in machine learning and prepare them for careers in this rapidly growing field.
Financial Analyst
Financial Analyst
Financial Analysts seeking to expand their skillset in data analysis and modeling may benefit from the AutoML con AutoSklearn y Google Colab course. By understanding the basics of autoML techniques, Financial Analysts can improve the accuracy and efficiency of their financial models, leading to more informed investment decisions.
For more career information including salaries, visit:
OpenCourser.com/course/izzz7n/automl
Reading list
We've selected six 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
AutoML con AutoSklearn y Google Colab.
Combines theoretical concepts with practical programming in Python, an industry-standard, cross-platform language suited for data science.
Introduces the core concepts and algorithms of machine learning, offering a concise overview of the most popular techniques and a review of the field’s history and development.
General introduction to the fundamentals of AI and its applications, providing a foundation for further learning in specialized areas like autoML.
Is an extensive guide to machine learning with R, a popular language for data analysis and statistical modeling, covering various techniques and applications.
This text provides a broad overview of intelligent systems, introducing the underlying concepts and their applications in engineering.
Offers a gentle introduction to machine learning, aimed at beginners with no prior knowledge in the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/izzz7n/automl
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Good to know
Helps learners apply their knowledge outside of a purely theoretical context
Provides a cost-efficient way to develop practical skills
Provides hands-on experience with autoML
Combines theoretical and practical knowledge
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