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Leire Ahedo
Este proyecto es un curso práctico y efectivo para aprender Machine Learning con Python. Aprenderás todos los pasos de desarrollo de un modelo y a evaluar su desempeño.
Aprenderás de manera práctica y aplicada a desarrollar un modelo completo de Machine learning, desde el pre-procesamiento de datos hasta la validación del modelo. También aprenderás a aplicar conceptos avanzados de machine learning como: pipelines, validación cruzada o XGBoost.
Register for this course and see more details by visiting:
OpenCourser.com/course/vkpr0y/machine
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches practical applications to develop a machine learning model
Covers advanced machine learning concepts like pipelines, cross-validation, and XGBoost
Provides hands-on experience in machine learning model development
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Reviews summary
Course with mixed reviews
This course focuses on practical application of machine learning concepts and includes hands-on exercises. However, some learners have expressed concerns about the quality of the explanations and the presence of errors in the provided material.
Course provides practical and applied learning.
"Aprenderás de manera práctica y aplicada a desarrollar un modelo completo de Machine learning,..."
Course materials contain errors.
"el notebook tiene errores que se pueden arreglar de manera simple,..."
Instructor explanations could be improved.
"no se explica mucho,..."
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 Machine Learning con Python. Nivel Avanzado with these
activities:
Review basic Python programming concepts
Show steps
A refresher on Python programming will help ensure that you have a strong foundation for the course.
Browse courses on
Python Programming
Show steps
-
Review online tutorials or documentation on Python basics.
-
Practice writing simple Python programs.
-
Complete practice problems or exercises to test your understanding.
Review lecture notes and assignments from previous coursework in machine learning or Python programming
Show steps
A quick refresher of the basics will bridge any gaps in your knowledge before starting the course.
Show steps
-
Gather lecture notes and assignments from previous courses.
-
Review the materials to refresh your memory.
-
Identify any areas where you need additional support.
Practice solving coding challenges on LeetCode
Show steps
Repetition and practice will build coding skills and reinforce knowledge of concepts covered in the course.
Show steps
-
Identify a coding challenge that aligns with a course concept.
-
Attempt to solve the challenge on your own.
-
Review your solution and compare it to example solutions.
-
Repeat the process with different challenges.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Join a study group to discuss course concepts and work on projects together
Show steps
Collaborating with peers will enhance your understanding of the material and provide support.
Show steps
-
Find a study group or create your own.
-
Meet regularly to discuss course concepts.
-
Work together on projects and assignments.
Create a blog post explaining a complex concept from the course
Show steps
Explaining a concept to others solidifies understanding and allows you to think critically about how the concept fits into the larger picture.
Show steps
-
Choose a concept from the course that you want to explain.
-
Research the concept thoroughly to ensure your understanding.
-
Write a clear and concise explanation of the concept.
-
Proofread and edit your blog post.
-
Publish your blog post and share it with others.
Follow online tutorials on advanced machine learning techniques
Show steps
Tutorials will supplement the course material and provide additional insights into advanced machine learning techniques.
Show steps
-
Identify online tutorials that cover advanced machine learning techniques.
-
Follow the tutorials step-by-step.
-
Implement the techniques in your own projects.
Mentor junior students in machine learning or Python programming
Show steps
Mentoring others will reinforce your understanding of the material and help you develop leadership skills.
Show steps
-
Identify opportunities to mentor junior students.
-
Provide guidance and support to your mentees.
-
Answer their questions and help them troubleshoot problems.
Contribute to an open-source machine learning project
Show steps
Contributing to an open-source project will provide valuable hands-on experience and expose you to best practices.
Browse courses on
Machine Learning
Show steps
-
Read the project's documentation and guidelines.
-
Identify an open-source machine learning project to contribute to.
-
Identify an area where you can make a meaningful contribution.
-
Implement your changes and submit a pull request.
Build a machine learning model and deploy it to a cloud platform
Show steps
Building and deploying a machine learning model will provide hands-on experience and insights into real-world applications.
Show steps
-
Choose a dataset and define the problem you want to solve.
-
Build a machine learning model using Python.
-
Deploy the model to a cloud platform.
-
Monitor and evaluate the performance of your model.
Review basic Python programming concepts
Show steps
A refresher on Python programming will help ensure that you have a strong foundation for the course.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review online tutorials or documentation on Python basics.
- Practice writing simple Python programs.
- Complete practice problems or exercises to test your understanding.
Review lecture notes and assignments from previous coursework in machine learning or Python programming
Show steps
A quick refresher of the basics will bridge any gaps in your knowledge before starting the course.
Show steps
Show steps
Show steps
- Gather lecture notes and assignments from previous courses.
- Review the materials to refresh your memory.
- Identify any areas where you need additional support.
Practice solving coding challenges on LeetCode
Show steps
Repetition and practice will build coding skills and reinforce knowledge of concepts covered in the course.
Show steps
Show steps
Show steps
- Identify a coding challenge that aligns with a course concept.
- Attempt to solve the challenge on your own.
- Review your solution and compare it to example solutions.
- Repeat the process with different challenges.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Join a study group to discuss course concepts and work on projects together
Show steps
Collaborating with peers will enhance your understanding of the material and provide support.
Show steps
Show steps
Show steps
- Find a study group or create your own.
- Meet regularly to discuss course concepts.
- Work together on projects and assignments.
Create a blog post explaining a complex concept from the course
Show steps
Explaining a concept to others solidifies understanding and allows you to think critically about how the concept fits into the larger picture.
Show steps
Show steps
Show steps
- Choose a concept from the course that you want to explain.
- Research the concept thoroughly to ensure your understanding.
- Write a clear and concise explanation of the concept.
- Proofread and edit your blog post.
- Publish your blog post and share it with others.
Follow online tutorials on advanced machine learning techniques
Show steps
Tutorials will supplement the course material and provide additional insights into advanced machine learning techniques.
Show steps
Show steps
Show steps
- Identify online tutorials that cover advanced machine learning techniques.
- Follow the tutorials step-by-step.
- Implement the techniques in your own projects.
Mentor junior students in machine learning or Python programming
Show steps
Mentoring others will reinforce your understanding of the material and help you develop leadership skills.
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior students.
- Provide guidance and support to your mentees.
- Answer their questions and help them troubleshoot problems.
Contribute to an open-source machine learning project
Show steps
Contributing to an open-source project will provide valuable hands-on experience and expose you to best practices.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Read the project's documentation and guidelines.
- Identify an open-source machine learning project to contribute to.
- Identify an area where you can make a meaningful contribution.
- Implement your changes and submit a pull request.
Build a machine learning model and deploy it to a cloud platform
Show steps
Building and deploying a machine learning model will provide hands-on experience and insights into real-world applications.
Show steps
Show steps
Show steps
- Choose a dataset and define the problem you want to solve.
- Build a machine learning model using Python.
- Deploy the model to a cloud platform.
- Monitor and evaluate the performance of your model.
Career center
Learners who complete Machine Learning con Python. Nivel Avanzado will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy Machine Learning models to solve complex problems. This course covers the entire process of developing Machine Learning models, making it an ideal starting point for aspiring Machine Learning Engineers. By learning to pre-process data, evaluate models, and apply advanced concepts, learners can build a strong foundation for success in this role.
Data Scientist
Data Scientist
Data Scientists gather and analyze large amounts of data to extract meaningful insights and create solutions to business problems. This course would help build a foundation for success in Data Science by teaching how to develop Machine Learning models, evaluate their performance, and apply advanced concepts such as pipelines and XGBoost. With the skills acquired, learners can become more competitive in this field.
Data Analyst
Data Analyst
Data Analysts clean, analyze, and interpret data to help businesses make informed decisions. While this course focuses on Machine Learning, it provides a strong foundation for Data Analysts who want to incorporate Machine Learning into their work. By learning how to develop and evaluate Machine Learning models, Data Analysts can become more effective in extracting insights from complex data.
Software Engineer
Software Engineer
Software Engineers design, develop, and test software applications. While this course is not directly geared towards software engineering, the skills it teaches are highly relevant to the field. By learning how to develop Machine Learning models, Software Engineers can become more proficient in creating intelligent and data-driven applications.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course can help build a foundation for success as a Quantitative Analyst by teaching how to develop and evaluate Machine Learning models. By learning to apply advanced concepts such as pipelines and XGBoost, learners can become more competitive in this highly specialized field.
Business Analyst
Business Analyst
Business Analysts use data and analysis to solve business problems. This course would help build a foundation for success in Business Analysis by teaching how to develop and evaluate Machine Learning models. By learning to apply advanced concepts such as pipelines and XGBoost, learners can become more effective in extracting insights from complex data and making data-driven recommendations.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of products. This course can help build a foundation for success as a Product Manager by teaching how to develop and evaluate Machine Learning models. By learning to apply advanced concepts such as pipelines and XGBoost, learners can become more effective in creating innovative and data-driven products.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve complex problems in business and industry. This course would help build a foundation for success in Operations Research by teaching how to develop and evaluate Machine Learning models. By learning to apply advanced concepts such as pipelines and XGBoost, learners can become more effective in optimizing processes and making data-driven decisions.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks to businesses and organizations. This course can help build a foundation for success as a Risk Analyst by teaching how to develop and evaluate Machine Learning models. By learning to apply advanced concepts such as pipelines and XGBoost, learners can become more effective in quantifying and mitigating risks.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment decisions. This course can help build a foundation for success as a Financial Analyst by teaching how to develop and evaluate Machine Learning models. By learning to apply advanced concepts such as pipelines and XGBoost, learners can become more proficient in analyzing financial data and making data-driven investment decisions.
Insurance Analyst
Insurance Analyst
Insurance Analysts assess risks and determine insurance premiums. This course can help build a foundation for success as an Insurance Analyst by teaching how to develop and evaluate Machine Learning models. By learning to apply advanced concepts such as pipelines and XGBoost, learners can become more effective in quantifying and pricing risks.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to solve problems. This course would help build a foundation for success as a Statistician by teaching how to develop and evaluate Machine Learning models. By learning to apply advanced concepts such as pipelines and XGBoost, learners can become more proficient in statistical modeling and data analysis.
Healthcare Analyst
Healthcare Analyst
Healthcare Analysts analyze data to improve patient care and reduce costs. This course can help build a foundation for success as a Healthcare Analyst by teaching how to develop and evaluate Machine Learning models. By learning to apply advanced concepts such as pipelines and XGBoost, learners can become more effective in analyzing healthcare data and making data-driven decisions.
Marketing Analyst
Marketing Analyst
Marketing Analysts analyze data to understand consumer behavior and improve marketing campaigns. This course can help build a foundation for success as a Marketing Analyst by teaching how to develop and evaluate Machine Learning models. By learning to apply advanced concepts such as pipelines and XGBoost, learners can become more effective in targeting and segmenting audiences.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines to ensure that data is available for analysis. While this course does not directly cover data engineering, the skills it teaches are highly relevant to the field. By learning how to develop Machine Learning models, Data Engineers can become more proficient in creating data-driven solutions.
For more career information including salaries, visit:
OpenCourser.com/course/vkpr0y/machine
Reading list
We've selected ten 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
Machine Learning con Python. Nivel Avanzado.
An advanced textbook covering the theoretical foundations of machine learning, offering a deep understanding of algorithms and their mathematical underpinnings.
Provides a comprehensive overview of machine learning concepts and techniques, with a focus on practical implementation using popular Python libraries.
Save
A classic textbook on machine learning, providing a comprehensive overview of the field from a theoretical and algorithmic perspective.
A comprehensive textbook on statistical learning, covering both theory and practice with a focus on predictive modeling.
A classic textbook on machine learning, providing a rigorous and comprehensive overview of the field, including advanced topics.
Covers the theory and application of probabilistic graphical models, providing a deeper understanding of Bayesian networks and their use in machine learning.
An introductory textbook on reinforcement learning, covering fundamental concepts and algorithms, providing a foundation for understanding more advanced topics.
Provides a comprehensive guide to deep learning, covering both theoretical concepts and practical implementation in Python.
Provides a hands-on and practical approach to machine learning, aimed at coders and developers.
Covers machine learning concepts and techniques using the R programming language, providing a practical and accessible approach.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vkpr0y/machine
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Good to know
Teaches practical applications to develop a machine learning model
Covers advanced machine learning concepts like pipelines, cross-validation, and XGBoost
Provides hands-on experience in machine learning model development
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