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Leire Ahedo
Este proyecto es un curso práctico y efectivo para aprender todo lo que necesitas saber acerca de autoML y Pycaret. Aprenderemos a generar un modelo predictivo de clasificación multiclase capaz de predecir el tipo de suelo en base a datos de satélite. Para...
Read more
Este proyecto es un curso práctico y efectivo para aprender todo lo que necesitas saber acerca de autoML y Pycaret. Aprenderemos a generar un modelo predictivo de clasificación multiclase capaz de predecir el tipo de suelo en base a datos de satélite. Para...
Read more
Este proyecto es un curso práctico y efectivo para aprender todo lo que necesitas saber acerca de autoML y Pycaret. Aprenderemos a generar un modelo predictivo de clasificación multiclase capaz de predecir el tipo de suelo en base a datos de satélite. Para ello, aprenderemos, de manera práctica, a generar múltiples modelos de ML y metamodelos, a evaluar su eficiencia, a desplegarlos en producción y a guardarlos en MlFlow, etc.
Register for this course and see more details by visiting:
OpenCourser.com/course/celxrg/clasificacion
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possible dealbreakers
Provides a practical learning experience by guiding learners through the development of a real-world predictive model
Leire Ahedo is a recognized expert in the field of machine learning and PyCaret
Teaches learners how to deploy models in production, a crucial skill for data scientists
Covers essential topics such as model evaluation, which is critical for ensuring model accuracy
Requires learners to have prior knowledge of Python and machine learning concepts
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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 Clasificación de datos de Satélites con autoML y Pycaret with these
activities:
Review basic linear algebra and calculus concepts
Show steps
Strengthen your foundation by reviewing essential mathematical concepts used in machine learning.
Browse courses on
Linear Algebra
Show steps
-
Identify key concepts in linear algebra and calculus
-
Review lecture notes and textbooks
-
Complete practice problems
Follow tutorials on Pycaret library for machine learning
Show steps
Deepen your understanding of the Pycaret library and its applications in machine learning.
Browse courses on
Machine Learning
Show steps
-
Find tutorials on the Pycaret library
-
Follow the tutorials step-by-step
-
Experiment with the code examples
Practice exercises on multi-class classification models
Show steps
Reinforce your understanding of multi-class classification models through hands-on practice exercises.
Browse courses on
Machine Learning
Show steps
-
Complete coding exercises on multi-class classification
-
Review solutions and identify areas for improvement
-
Repeat exercises to strengthen understanding
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a data visualization of soil types based on satellite data
Show steps
Create a compelling visual representation of soil types to solidify understanding of data generated from satellite images.
Browse courses on
Data Visualization
Show steps
-
Gather data from satellite imagery
-
Preprocess and clean the data
-
Train a machine learning model to predict soil types
-
Visualize the data using a tool such as Tableau or Power BI
Kaggle competition: Soil classification using satellite data
Show steps
Participate in a real-world competition to test and refine your understanding of soil classification using satellite data.
Browse courses on
Machine Learning
Show steps
-
Join the Kaggle competition
-
Develop a machine learning model
-
Submit your model to the competition
-
Analyze the results
Develop a blog post on best practices for soil classification using machine learning
Show steps
Enhance your understanding by summarizing and sharing your insights on soil classification using machine learning, reinforcing key concepts.
Browse courses on
Machine Learning
Show steps
-
Research best practices for soil classification using machine learning
-
Organize your findings into a coherent outline
-
Write and edit your blog post
-
Publish and promote your blog post
Build a machine learning model to predict soil types in your local area
Show steps
Apply your learnings by developing a practical machine learning solution for soil classification, enhancing your understanding of real-world applications.
Browse courses on
Machine Learning
Show steps
-
Collect data on soil types and satellite imagery for your local area
-
Train a machine learning model using the data
-
Evaluate the performance of your model
-
Deploy your model and make predictions
Review basic linear algebra and calculus concepts
Show steps
Strengthen your foundation by reviewing essential mathematical concepts used in machine learning.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Identify key concepts in linear algebra and calculus
- Review lecture notes and textbooks
- Complete practice problems
Follow tutorials on Pycaret library for machine learning
Show steps
Deepen your understanding of the Pycaret library and its applications in machine learning.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find tutorials on the Pycaret library
- Follow the tutorials step-by-step
- Experiment with the code examples
Practice exercises on multi-class classification models
Show steps
Reinforce your understanding of multi-class classification models through hands-on practice exercises.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Complete coding exercises on multi-class classification
- Review solutions and identify areas for improvement
- Repeat exercises to strengthen understanding
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a data visualization of soil types based on satellite data
Show steps
Create a compelling visual representation of soil types to solidify understanding of data generated from satellite images.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather data from satellite imagery
- Preprocess and clean the data
- Train a machine learning model to predict soil types
- Visualize the data using a tool such as Tableau or Power BI
Kaggle competition: Soil classification using satellite data
Show steps
Participate in a real-world competition to test and refine your understanding of soil classification using satellite data.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Join the Kaggle competition
- Develop a machine learning model
- Submit your model to the competition
- Analyze the results
Develop a blog post on best practices for soil classification using machine learning
Show steps
Enhance your understanding by summarizing and sharing your insights on soil classification using machine learning, reinforcing key concepts.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Research best practices for soil classification using machine learning
- Organize your findings into a coherent outline
- Write and edit your blog post
- Publish and promote your blog post
Build a machine learning model to predict soil types in your local area
Show steps
Apply your learnings by developing a practical machine learning solution for soil classification, enhancing your understanding of real-world applications.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Collect data on soil types and satellite imagery for your local area
- Train a machine learning model using the data
- Evaluate the performance of your model
- Deploy your model and make predictions
Career center
Learners who complete Clasificación de datos de Satélites con autoML y Pycaret will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. They work on projects ranging from natural language processing to computer vision. This course provides a solid foundation in machine learning and artificial intelligence, which are essential skills for success in this field.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. This course provides a strong foundation in data science, which is essential for success in this field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work on a wide range of projects, from mobile apps to enterprise software. This course provides a strong foundation in software engineering, which is essential for success in this field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They work on projects ranging from risk management to portfolio optimization. This course provides a strong foundation in mathematics and statistics, which are essential skills for success in this field.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They work on projects ranging from public health to market research. This course provides a strong foundation in statistics, which is essential for success in this field.
Business Analyst
Business Analyst
Business Analysts help businesses make better decisions by analyzing data and providing insights. This course provides a strong foundation in business analysis, which is essential for success in this field.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work on a wide range of projects, from consumer products to enterprise software. This course provides a strong foundation in product management, which is essential for success in this field.
Marketing Analyst
Marketing Analyst
Marketing Analysts help businesses understand their customers and develop marketing campaigns. This course provides a strong foundation in marketing analytics, which is essential for success in this field.
Financial Analyst
Financial Analyst
Financial Analysts help businesses make investment decisions. They analyze financial data and make recommendations on which investments to make. This course provides a strong foundation in financial analysis, which is essential for success in this field.
For more career information including salaries, visit:
OpenCourser.com/course/celxrg/clasificacion
Reading list
We've selected 13 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
Clasificación de datos de Satélites con autoML y Pycaret.
Essential reference for data science practitioners, covering data manipulation, visualization, and machine learning techniques in Python.
In-depth coverage of machine learning algorithms and techniques using popular Python libraries, providing a comprehensive understanding of the underlying principles.
Practical and entertaining introduction to Python, covering data manipulation, automation, and scripting, providing a solid foundation for working with autoML tools.
Practical guide to machine learning using Python, providing step-by-step instructions and real-world examples.
Comprehensive guide to machine learning using Python, covering data preprocessing, model selection, and evaluation.
Comprehensive guide to machine learning using R, providing a strong foundation for working with R-based autoML tools.
Comprehensive guide to deep learning using TensorFlow and Keras, covering neural networks, image recognition, and natural language processing.
Textbook-style introduction to machine learning algorithms, covering supervised and unsupervised learning, providing a comprehensive understanding of the underlying theory.
Practical guide to advanced analytics using Spark, covering data processing, machine learning, and graph algorithms.
Hands-on introduction to data science using Python, providing a comprehensive overview of the data science workflow.
Save
Classical reference work on pattern recognition and machine learning, providing a theoretical foundation for understanding autoML techniques.
Detailed overview of machine learning algorithms, covering their underlying theory and implementation, providing a strong theoretical foundation.
Textbook-style introduction to machine learning algorithms, covering supervised and unsupervised learning, providing a mathematical and theoretical foundation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/celxrg/clasificacion
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Good to know
Provides a practical learning experience by guiding learners through the development of a real-world predictive model
Leire Ahedo is a recognized expert in the field of machine learning and PyCaret
Teaches learners how to deploy models in production, a crucial skill for data scientists
Covers essential topics such as model evaluation, which is critical for ensuring model accuracy
Requires learners to have prior knowledge of Python and machine learning concepts
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