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Leire Ahedo
Este proyecto es un curso práctico para crear una aplicación web con un modelo de aprendizaje automático. Aprenderemos desde las bases a utilizar librerías y herramientas como Pycaret, Streamlit, Heroku y GitHub, entre otros.
Gracias a este curso desarrollarás tu propio modelo de ML y página web y lo desplegarás en un servidor de Heroku.
Register for this course and see more details by visiting:
OpenCourser.com/course/emcf1k/desarrollar
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for individuals with some background in Python, machine learning, and web development
Taught by an instructor who has experience in the application of machine learning
Provides hands-on practice in developing a machine learning model and deploying it on a web application
Utilizes industry-standard tools such as Pycaret, Streamlit, Heroku, and GitHub
Save this course
Save Desarrollar una aplicación web de ML con PyCaret y Streamlit to your list so you can find it easily later:
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Reviews summary
Fun and practical ml web app
This course teaches you everything you need to know to build and deploy an ML web app using PyCaret, Streamlit, Heroku, and GitHub. With just two reviews submitted in total, the course appears to be well-received with one glowing review highly recommending the course.
Highly recommended course
"Muy bueno, lo recomiendo."
Access problems
"necesito que me vuelvan a habilitar el curso no tengo acceso"
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 Desarrollar una aplicación web de ML con PyCaret y Streamlit with these
activities:
Connect with experienced professionals in ML
Show steps
Seek guidance and insights from experienced professionals to accelerate your learning and gain valuable perspectives.
Browse courses on
Machine Learning
Show steps
-
Identify potential mentors in your field.
-
Reach out to them and request mentorship.
-
Schedule regular meetings to discuss your progress and seek advice.
Read about ML in Python
Show steps
Develop a foundational understanding of ML in Python through a comprehensive book.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Obtain a copy of the book.
-
Read chapters 1-3.
-
Complete the exercises in chapters 1-3.
Create a GitHub repository for your project
Show steps
Showcase your work and collaborate effectively by creating a GitHub repository for your project.
Browse courses on
GitHub
Show steps
-
Create a GitHub account.
-
Create a new repository.
-
Push your project code to the repository.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice coding Pycaret models
Show steps
Solidify your understanding of Pycaret through hands-on coding exercises.
Browse courses on
Pycaret
Show steps
-
Install Pycaret.
-
Follow the Pycaret tutorial.
-
Create a simple ML model using Pycaret.
Follow tutorials to enhance your understanding of Heroku
Show steps
Expand your knowledge of Heroku through guided tutorials, improving your ability to deploy and manage your projects.
Browse courses on
Heroku
Show steps
-
Find tutorials on Heroku's website.
-
Follow the tutorials to learn about different aspects of Heroku.
-
Apply what you learn to your own projects.
Attend ML hackathons or workshops
Show steps
Engage with a community of ML enthusiasts, learn from experts, and push your skills to the next level.
Browse courses on
Machine Learning
Show steps
-
Research upcoming ML hackathons or workshops.
-
Register for and attend the events.
-
Actively participate in discussions and hands-on activities.
Build a Streamlit dashboard for your ML model
Show steps
Enhance your project by creating an interactive dashboard to visualize and interact with your ML model.
Browse courses on
Streamlit
Show steps
-
Learn the basics of Streamlit.
-
Design the layout of your dashboard.
-
Integrate your ML model into your dashboard.
-
Deploy your dashboard to Heroku.
Develop a personal ML project
Show steps
Apply your learning to a real-world problem by developing your own ML project, fostering creativity and problem-solving skills.
Browse courses on
Machine Learning
Show steps
-
Define the problem you want to solve.
-
Gather data and explore it.
-
Choose and train a machine learning model.
-
Evaluate and fine-tune your model.
-
Deploy your model and monitor its performance.
Connect with experienced professionals in ML
Show steps
Seek guidance and insights from experienced professionals to accelerate your learning and gain valuable perspectives.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify potential mentors in your field.
- Reach out to them and request mentorship.
- Schedule regular meetings to discuss your progress and seek advice.
Read about ML in Python
Show steps
Develop a foundational understanding of ML in Python through a comprehensive book.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of the book.
- Read chapters 1-3.
- Complete the exercises in chapters 1-3.
Create a GitHub repository for your project
Show steps
Showcase your work and collaborate effectively by creating a GitHub repository for your project.
Browse courses on
GitHub
Show steps
Show steps
Show steps
- Create a GitHub account.
- Create a new repository.
- Push your project code to the repository.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice coding Pycaret models
Show steps
Solidify your understanding of Pycaret through hands-on coding exercises.
Browse courses on
Pycaret
Show steps
Show steps
Show steps
- Install Pycaret.
- Follow the Pycaret tutorial.
- Create a simple ML model using Pycaret.
Follow tutorials to enhance your understanding of Heroku
Show steps
Expand your knowledge of Heroku through guided tutorials, improving your ability to deploy and manage your projects.
Browse courses on
Heroku
Show steps
Show steps
Show steps
- Find tutorials on Heroku's website.
- Follow the tutorials to learn about different aspects of Heroku.
- Apply what you learn to your own projects.
Attend ML hackathons or workshops
Show steps
Engage with a community of ML enthusiasts, learn from experts, and push your skills to the next level.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Research upcoming ML hackathons or workshops.
- Register for and attend the events.
- Actively participate in discussions and hands-on activities.
Build a Streamlit dashboard for your ML model
Show steps
Enhance your project by creating an interactive dashboard to visualize and interact with your ML model.
Browse courses on
Streamlit
Show steps
Show steps
Show steps
- Learn the basics of Streamlit.
- Design the layout of your dashboard.
- Integrate your ML model into your dashboard.
- Deploy your dashboard to Heroku.
Develop a personal ML project
Show steps
Apply your learning to a real-world problem by developing your own ML project, fostering creativity and problem-solving skills.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Define the problem you want to solve.
- Gather data and explore it.
- Choose and train a machine learning model.
- Evaluate and fine-tune your model.
- Deploy your model and monitor its performance.
Career center
Learners who complete Desarrollar una aplicación web de ML con PyCaret y Streamlit will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops, builds, and maintains machine learning models. This course provides a solid foundation in machine learning, including how to train and deploy models. It also covers how to use popular machine learning libraries such as PyCaret and Streamlit.
Data Scientist
Data Scientist
A Data Scientist collects, analyzes, and interprets data to extract meaningful insights. This course provides a strong foundation in data science, including how to use machine learning to solve real-world problems. It also covers how to use popular data science tools such as PyCaret and Streamlit.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course provides a solid foundation in software engineering, including how to use popular programming languages and tools such as PyCaret and Streamlit. It also covers how to design and develop web applications.
Web Developer
Web Developer
A Web Developer designs and develops websites. This course provides a strong foundation in web development, including how to use popular programming languages and tools such as PyCaret and Streamlit. It also covers how to design and develop responsive websites.
Project Manager
Project Manager
A Project Manager plans and executes projects to achieve specific goals. This course provides a solid foundation in project management, including how to use machine learning to solve real-world problems. It also covers how to use popular project management tools such as PyCaret and Streamlit.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes and develops solutions to improve efficiency and effectiveness. This course provides a solid foundation in business analysis, including how to use machine learning to solve real-world problems. It also covers how to use popular business analysis tools such as PyCaret and Streamlit.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to identify trends and patterns. This course provides a solid foundation in data analysis, including how to use machine learning to solve real-world problems. It also covers how to use popular data analysis tools such as PyCaret and Streamlit.
Product Manager
Product Manager
A Product Manager plans and executes the development of new products and features. This course provides a solid foundation in product management, including how to use machine learning to solve real-world problems. It also covers how to use popular product management tools such as PyCaret and Streamlit.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst analyzes data to identify trends and patterns that can help businesses make better decisions. This course provides a solid foundation in business intelligence, including how to use machine learning to solve real-world problems. It also covers how to use popular business intelligence tools such as PyCaret and Streamlit.
Information Technology Specialist
Information Technology Specialist
An Information Technology Specialist provides technical support and maintenance to computer systems and networks. This course provides a solid foundation in information technology, including how to use popular information technology tools such as PyCaret and Streamlit.
Data Engineer
Data Engineer
A Data Engineer designs and builds data pipelines to collect, store, and process data. This course provides a solid foundation in data engineering, including how to use popular data engineering tools such as PyCaret and Streamlit.
Systems Analyst
Systems Analyst
A Systems Analyst analyzes and designs computer systems to meet business needs. This course provides a solid foundation in systems analysis, including how to use popular systems analysis tools such as PyCaret and Streamlit.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases to ensure data integrity and security. This course provides a solid foundation in database administration, including how to use popular database management tools such as PyCaret and Streamlit.
Computer Programmer
Computer Programmer
A Computer Programmer writes and maintains computer programs. This course provides a solid foundation in computer programming, including how to use popular programming languages and tools such as PyCaret and Streamlit.
Technical Writer
Technical Writer
A Technical Writer creates and maintains technical documentation. This course may be useful for learning how to effectively communicate technical information to a variety of audiences. It covers how to use popular documentation tools such as PyCaret and Streamlit.
For more career information including salaries, visit:
OpenCourser.com/course/emcf1k/desarrollar
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
Desarrollar una aplicación web de ML con PyCaret y Streamlit.
Serves as a comprehensive guide to Python for data analysis. It covers data manipulation, data visualization, and machine learning with a focus on practical applications. It's a valuable reference for anyone working with data in Python.
Introduces readers to programming with Python, focusing on automating tasks. It provides a solid foundation in Python basics and valuable resource for beginners looking to enhance their Python skills.
Save
Introduces the fundamentals of artificial intelligence using Python. It covers topics such as natural language processing, computer vision, and machine learning, providing a broad overview of AI concepts and applications.
Serves as a comprehensive guide to deep learning with Python, providing a deep dive into neural networks and their applications. It's a valuable resource for individuals interested in exploring the more advanced aspects of machine learning.
Provides a comprehensive overview of the machine learning landscape, covering different types of machine learning algorithms, their strengths and weaknesses, and their applications in various domains. It's a valuable resource for staying up-to-date on the latest developments in machine learning.
Explores advanced topics in statistical learning, with a focus on sparsity and its applications in machine learning. It covers topics such as model selection, regularization techniques, and compressed sensing, providing a deep understanding of the theoretical foundations of machine learning.
Offers a hands-on introduction to machine learning, providing practical examples and code snippets. It covers essential concepts and algorithms, and guides readers through building and deploying machine learning models.
Provides a comprehensive guide to machine learning with Python, covering a wide range of topics, including data preparation, feature engineering, model selection, and evaluation. It's a valuable resource for individuals looking to build a strong foundation in machine learning with Python.
Provides a gentle introduction to data science, covering essential concepts and techniques, such as data manipulation, data visualization, and machine learning. It's written for beginners with little to no prior experience in data science.
Provides a comprehensive guide to machine learning with R, covering data preparation, model building, and evaluation. It offers a practical approach to machine learning and is suitable for both beginners and experienced practitioners.
Offers a playful introduction to machine learning, providing hands-on examples and code snippets. It covers essential concepts and algorithms, and guides readers through building and deploying machine learning models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/emcf1k/desarrollar
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Good to know
Suitable for individuals with some background in Python, machine learning, and web development
Taught by an instructor who has experience in the application of machine learning
Provides hands-on practice in developing a machine learning model and deploying it on a web application
Utilizes industry-standard tools such as Pycaret, Streamlit, Heroku, and GitHub
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