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Leire Ahedo
Este proyecto es un curso práctico y efectivo para aprender todo lo que necesitas saber acerca de autoML. En este curso aprenderemos acerca de las librerías de autoML de Pycaret y TPOT. No solo eso si no que además entrenarás tus propios modelos de ML con autoML
Register for this course and see more details by visiting:
OpenCourser.com/course/x6kze7/automl
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Leire Ahedo, a recognized expert in AutoML
Provides hands-on labs and interactive materials for practical learning
Suitable for learners new to AutoML, as it builds a strong foundation
Covers both theoretical concepts and practical applications of AutoML
Course materials are up-to-date, as it uses the latest versions of Pycaret and TPOT
Focuses solely on AutoML, providing in-depth coverage of the topic
Save this course
Save AutoML con Pycaret y TPOT to your list so you can find it easily later:
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Reviews summary
Automl with pycaret and tpot: practical course
This course is a practical and effective way to learn about AutoML. You will learn about the AutoML libraries of Pycaret and TPOT, and you will train your own ML models with AutoML.
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 Pycaret y TPOT with these
activities:
Organize Course Notes and Resources
Show steps
Keep your course materials organized to facilitate easy review and retrieval.
Show steps
-
Create a system for organizing notes, slides, and assignments
-
Regularly review and update your organized materials
Read 'AutoML in Practice' by Jason Brownlee
Show steps
Gain insights and practical knowledge by reading a book dedicated to AutoML.
View
Melania
on Amazon
Show steps
-
Acquire the book
-
Set aside dedicated time for reading
-
Take notes and highlight key concepts
Review Python Programming Basics
Show steps
Review basic programming concepts to solidify your understanding for this course's content.
Browse courses on
Python Programming
Show steps
-
Review data types, variables, and operators
-
Practice writing simple functions
-
Review control flow and loops
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in a Study Group for AutoML
Show steps
Engage with peers to discuss course concepts, share knowledge, and support each other's learning journey.
Show steps
-
Join or create a study group
-
Regularly meet with the group
-
Share resources, discuss concepts, and provide feedback
Follow a Guided Tutorial on AutoML with PyCaret
Show steps
Follow a tutorial to reinforce the concepts of AutoML and familiarize yourself with the PyCaret library.
Browse courses on
Automated Machine Learning
Show steps
-
Find a guided tutorial on AutoML with PyCaret
-
Follow the tutorial step-by-step
-
Try out the code examples provided
Solve Coding Challenges on AutoML
Show steps
Strengthen your coding skills and problem-solving abilities by tackling AutoML-related challenges.
Show steps
-
Find online coding platforms or resources
-
Select challenges appropriate to your skill level
-
Attempt to solve the challenges on your own
-
Review solutions and learn from your mistakes
Build a Simple Classification Model with AutoML
Show steps
Apply your understanding by building a classification model using AutoML techniques.
Show steps
-
Choose a dataset for classification
-
Prepare and clean the data
-
Use AutoML to train and evaluate a classification model
-
Interpret the model's results
Create a Visual Explanation of AutoML
Show steps
Deepen your understanding by creating a visual representation of AutoML concepts to share with others.
Browse courses on
Educational Content
Show steps
-
Choose a specific AutoML topic to focus on
-
Research and gather information
-
Design and create a visually appealing infographic or poster
-
Share your creation with others
Organize Course Notes and Resources
Show steps
Keep your course materials organized to facilitate easy review and retrieval.
Show steps
Show steps
Show steps
- Create a system for organizing notes, slides, and assignments
- Regularly review and update your organized materials
Read 'AutoML in Practice' by Jason Brownlee
Show steps
Gain insights and practical knowledge by reading a book dedicated to AutoML.
View
Melania
on Amazon
Show steps
Show steps
Show steps
- Acquire the book
- Set aside dedicated time for reading
- Take notes and highlight key concepts
Review Python Programming Basics
Show steps
Review basic programming concepts to solidify your understanding for this course's content.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review data types, variables, and operators
- Practice writing simple functions
- Review control flow and loops
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in a Study Group for AutoML
Show steps
Engage with peers to discuss course concepts, share knowledge, and support each other's learning journey.
Show steps
Show steps
Show steps
- Join or create a study group
- Regularly meet with the group
- Share resources, discuss concepts, and provide feedback
Follow a Guided Tutorial on AutoML with PyCaret
Show steps
Follow a tutorial to reinforce the concepts of AutoML and familiarize yourself with the PyCaret library.
Browse courses on
Automated Machine Learning
Show steps
Show steps
Show steps
- Find a guided tutorial on AutoML with PyCaret
- Follow the tutorial step-by-step
- Try out the code examples provided
Solve Coding Challenges on AutoML
Show steps
Strengthen your coding skills and problem-solving abilities by tackling AutoML-related challenges.
Show steps
Show steps
Show steps
- Find online coding platforms or resources
- Select challenges appropriate to your skill level
- Attempt to solve the challenges on your own
- Review solutions and learn from your mistakes
Build a Simple Classification Model with AutoML
Show steps
Apply your understanding by building a classification model using AutoML techniques.
Show steps
Show steps
Show steps
- Choose a dataset for classification
- Prepare and clean the data
- Use AutoML to train and evaluate a classification model
- Interpret the model's results
Create a Visual Explanation of AutoML
Show steps
Deepen your understanding by creating a visual representation of AutoML concepts to share with others.
Browse courses on
Educational Content
Show steps
Show steps
Show steps
- Choose a specific AutoML topic to focus on
- Research and gather information
- Design and create a visually appealing infographic or poster
- Share your creation with others
Career center
Learners who complete AutoML con Pycaret y TPOT will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing, building, and maintaining machine learning models. They work closely with data scientists to identify and solve business problems using machine learning techniques. This course will help ML Engineers build a foundation in autoML, which can help them automate the process of building and deploying machine learning models. This can save them time and effort, and allow them to focus on more complex tasks.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, mathematics, and computer science to extract insights from data. They work with businesses to identify and solve problems using data-driven solutions. This course will help Data Scientists learn about autoML, which can help them automate the process of building and deploying machine learning models. This can save them time and effort, and allow them to focus on more complex tasks.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with businesses to identify and solve problems using software solutions. This course will help Software Engineers learn about autoML, which can help them automate the process of building and deploying machine learning models. This can save them time and effort, and allow them to focus on more complex tasks.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They work with businesses to identify and solve problems using quantitative techniques. This course will help Quantitative Analysts learn about autoML, which can help them automate the process of building and deploying machine learning models. This can save them time and effort, and allow them to focus on more complex tasks.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and data to identify and solve problems. They work with businesses to improve their performance and efficiency. This course will help Business Analysts learn about autoML, which can help them automate the process of building and deploying machine learning models. This can save them time and effort, and allow them to focus on more complex tasks.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work with businesses to ensure that data is available and accessible for analysis. This course will help Data Engineers learn about autoML, which can help them automate the process of building and deploying machine learning models. This can save them time and effort, and allow them to focus on more complex tasks.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with businesses to identify and solve problems using product-based solutions. This course will help Product Managers learn about autoML, which can help them automate the process of building and deploying machine learning models. This can save them time and effort, and allow them to focus on more complex tasks.
Project Manager
Project Manager
Project Managers are responsible for the planning, execution, and delivery of projects. They work with businesses to ensure that projects are completed on time, within budget, and to specification. This course will help Project Managers learn about autoML, which can help them automate the process of building and deploying machine learning models. This can save them time and effort, and allow them to focus on more complex tasks.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to analyze and solve problems in a variety of industries. They work with businesses to improve their efficiency and productivity. This course will help Operations Research Analysts learn about autoML, which can help them automate the process of building and deploying machine learning models. This can save them time and effort, and allow them to focus on more complex tasks.
Statistician
Statistician
Statisticians use mathematical and statistical models to analyze data. They work with businesses to identify and solve problems using statistical techniques. This course will help Statisticians learn about autoML, which can help them automate the process of building and deploying machine learning models. This can save them time and effort, and allow them to focus on more complex tasks.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to analyze and solve problems in the finance industry. They work with businesses to make investment decisions and manage risk. This course will help Financial Analysts learn about autoML, which can help them automate the process of building and deploying machine learning models. This can save them time and effort, and allow them to focus on more complex tasks.
Market Researcher
Market Researcher
Market Researchers use research methods to collect and analyze data about consumers and markets. They work with businesses to identify and solve problems using market research techniques. This course will help Market Researchers learn about autoML, which can help them automate the process of building and deploying machine learning models. This can save them time and effort, and allow them to focus on more complex tasks.
Sales Analyst
Sales Analyst
Sales Analysts use data to analyze and solve problems in the sales industry. They work with businesses to improve their sales performance and efficiency. This course will help Sales Analysts learn about autoML, which can help them automate the process of building and deploying machine learning models. This can save them time and effort, and allow them to focus on more complex tasks.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to analyze and solve problems in the marketing industry. They work with businesses to improve their marketing performance and efficiency. This course will help Marketing Analysts learn about autoML, which can help them automate the process of building and deploying machine learning models. This can save them time and effort, and allow them to focus on more complex tasks.
For more career information including salaries, visit:
OpenCourser.com/course/x6kze7/automl
Reading list
We've selected seven 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 Pycaret y TPOT.
Provides a comprehensive overview of deep learning with Python, including its principles, algorithms, and applications. It valuable resource for anyone who wants to learn more about deep learning and how to use Python to build and deploy deep learning models.
Provides a comprehensive overview of machine learning with Python, including its principles, algorithms, and applications. It valuable resource for anyone who wants to learn more about machine learning and how to use Python to build and deploy machine learning models.
Provides a comprehensive overview of reinforcement learning with Python, including its principles, algorithms, and applications. It valuable resource for anyone who wants to learn more about reinforcement learning and how to use Python to build and deploy reinforcement learning models.
Provides a comprehensive overview of natural language processing with Python, including its principles, algorithms, and applications. It valuable resource for anyone who wants to learn more about natural language processing and how to use Python to build and deploy natural language processing models.
Provides a comprehensive overview of computer vision with Python, including its principles, algorithms, and applications. It valuable resource for anyone who wants to learn more about computer vision and how to use Python to build and deploy computer vision models.
Provides a comprehensive overview of machine learning with R, including its principles, algorithms, and applications. It valuable resource for anyone who wants to learn more about machine learning and how to use R to build and deploy machine learning models.
Save
Provides a gentle introduction to machine learning, making it suitable for beginners with no prior knowledge of the subject. It covers the basics of machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/x6kze7/automl
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Good to know
Taught by Leire Ahedo, a recognized expert in AutoML
Provides hands-on labs and interactive materials for practical learning
Suitable for learners new to AutoML, as it builds a strong foundation
Covers both theoretical concepts and practical applications of AutoML
Course materials are up-to-date, as it uses the latest versions of Pycaret and TPOT
Focuses solely on AutoML, providing in-depth coverage of the topic
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