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Ryan Ahmed
Hello everyone and welcome to this new hands-on project on AutoML with AutoGluon. In this project, we will use a power library known as AutoGluon to train several machine learning models to solve classification type problems. AutoGluon is the library behind Amazon web services autopilot, and it allows for quick prototyping of several powerful models using a few lines of code. You can add this project to your portfolio of projects which is essential for your next job interview.
Register for this course and see more details by visiting:
OpenCourser.com/course/6d8hpe/auto
What's inside
Syllabus
Project Overview
Hello everyone and welcome to this new hands-on project on AutoML with AutoGluon. In this project, we will use a power library known as AutoGluon to train several machine learning models to solve classification type problems. AutoGluon is the library behind Amazon web services autopilot, and it allows for quick prototyping of several powerful models using a few lines of code. You can add this project to your portfolio of projects which is essential for your next job interview.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops machine learning modeling skills using a few lines of code, which is highly relevant to industry
Covers Amazon Web Service AutoPilot, which is widely used in industry
Enhances portfolio projects for job interviews
Save this course
Save Auto Machine Learning (AutoML) Using AutoGluon to your list so you can find it easily later:
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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 Auto Machine Learning (AutoML) Using AutoGluon with these
activities:
Review Python
Show steps
Reviews the basics of python, including data structures and techniques to solve and approach common problems related to data science
Browse courses on
Python
Show steps
-
Revisit the basics of Python such as data types and control flow
-
Practice solving small programming problems using Python
-
Try to solve problems from various topics that you'll learn in the course
Revisit basic machine learning concepts
Show steps
Refreshes your understanding of fundamental machine learning concepts, ensuring you have a strong foundation before starting the course
Browse courses on
Machine Learning
Show steps
-
Review key concepts such as supervised and unsupervised learning
-
Go through examples and case studies to reinforce your understanding
Follow beginner-friendly tutorials on AutoML
Show steps
Provides a gentle introduction to AutoML and its applications, helping you understand the core concepts before delving into the course material
Browse courses on
AutoML
Show steps
-
Find beginner-friendly tutorials on AutoML
-
Follow the tutorials step-by-step, experimenting with different settings and datasets
Six other activities
Expand to see all activities and additional details
Show all nine activities
Attend meetups or conferences on AutoML
Show steps
Connects you with professionals in the field, exposing you to different perspectives and industry trends
Browse courses on
AutoML
Show steps
-
Find local meetups or conferences related to AutoML
-
Attend the event and engage in discussions
-
Follow up with interesting contacts you make
Solve AutoML practice problems
Show steps
Provides hands-on experience with AutoML, reinforcing the concepts learned in the course and building your problem-solving skills
Browse courses on
AutoML
Show steps
-
Find a collection of AutoML practice problems
-
Attempt to solve the problems using the techniques you've learned
-
Review your solutions and identify areas for improvement
Seek guidance from experienced AutoML professionals
Show steps
Connects you with experts who can provide personalized guidance, accelerate your learning, and support your career growth
Browse courses on
AutoML
Show steps
-
Identify potential mentors who are experienced in AutoML
-
Reach out and introduce yourself
-
Schedule regular meetings to discuss your progress and seek advice
Build a simple AutoML project
Show steps
Provides an opportunity to apply your AutoML skills to a practical problem, deepening your understanding and enhancing your portfolio
Show steps
-
Identify a suitable problem or dataset for your project
-
Prepare and clean the data
-
Apply AutoML techniques to train and evaluate models
-
Write a report summarizing your findings and insights
Contribute to open-source AutoML projects
Show steps
Provides hands-on experience with real-world AutoML projects, promoting collaboration and deepening your technical skills
Browse courses on
AutoML
Show steps
-
Identify open-source AutoML projects that align with your interests
-
Read the documentation and familiarize yourself with the project
-
Make contributions, such as fixing bugs or implementing new features
Attend hands-on AutoML workshops
Show steps
Provides an immersive learning experience where you can learn from experts, experiment with real-world datasets, and build practical skills
Browse courses on
AutoML
Show steps
-
Find upcoming AutoML workshops
-
Register for the workshop and prepare accordingly
-
Actively participate in the workshop and engage with the instructors
Review Python
Show steps
Reviews the basics of python, including data structures and techniques to solve and approach common problems related to data science
Browse courses on
Python
Show steps
Show steps
Show steps
- Revisit the basics of Python such as data types and control flow
- Practice solving small programming problems using Python
- Try to solve problems from various topics that you'll learn in the course
Revisit basic machine learning concepts
Show steps
Refreshes your understanding of fundamental machine learning concepts, ensuring you have a strong foundation before starting the course
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review key concepts such as supervised and unsupervised learning
- Go through examples and case studies to reinforce your understanding
Follow beginner-friendly tutorials on AutoML
Show steps
Provides a gentle introduction to AutoML and its applications, helping you understand the core concepts before delving into the course material
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Find beginner-friendly tutorials on AutoML
- Follow the tutorials step-by-step, experimenting with different settings and datasets
Six other activities
Expand to see all activities and additional details
Show all nine activities
Attend meetups or conferences on AutoML
Show steps
Connects you with professionals in the field, exposing you to different perspectives and industry trends
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Find local meetups or conferences related to AutoML
- Attend the event and engage in discussions
- Follow up with interesting contacts you make
Solve AutoML practice problems
Show steps
Provides hands-on experience with AutoML, reinforcing the concepts learned in the course and building your problem-solving skills
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Find a collection of AutoML practice problems
- Attempt to solve the problems using the techniques you've learned
- Review your solutions and identify areas for improvement
Seek guidance from experienced AutoML professionals
Show steps
Connects you with experts who can provide personalized guidance, accelerate your learning, and support your career growth
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Identify potential mentors who are experienced in AutoML
- Reach out and introduce yourself
- Schedule regular meetings to discuss your progress and seek advice
Build a simple AutoML project
Show steps
Provides an opportunity to apply your AutoML skills to a practical problem, deepening your understanding and enhancing your portfolio
Show steps
Show steps
Show steps
- Identify a suitable problem or dataset for your project
- Prepare and clean the data
- Apply AutoML techniques to train and evaluate models
- Write a report summarizing your findings and insights
Contribute to open-source AutoML projects
Show steps
Provides hands-on experience with real-world AutoML projects, promoting collaboration and deepening your technical skills
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Identify open-source AutoML projects that align with your interests
- Read the documentation and familiarize yourself with the project
- Make contributions, such as fixing bugs or implementing new features
Attend hands-on AutoML workshops
Show steps
Provides an immersive learning experience where you can learn from experts, experiment with real-world datasets, and build practical skills
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Find upcoming AutoML workshops
- Register for the workshop and prepare accordingly
- Actively participate in the workshop and engage with the instructors
Career center
Learners who complete Auto Machine Learning (AutoML) Using AutoGluon will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers work with large datasets to develop and deploy machine learning models that can perform predictions or classifications. They may use libraries like AutoGluon to automate the process of model training and selection. This course provides a good foundation in using AutoGluon, which can be useful for those looking to enter this field.
Data Scientist
Data Scientist
Data Scientists use machine learning and statistical techniques to extract insights from data. They may use libraries like AutoGluon to quickly prototype and evaluate different models. This course provides a good introduction to using AutoGluon, which can be useful for those looking to enter this field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They may use libraries like AutoGluon to automate the process of model training and selection. This course provides a good foundation in using AutoGluon, which can be useful for those looking to enter this field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They may use libraries like AutoGluon to automate the process of model training and selection. This course provides a good foundation in using AutoGluon, which can be useful for those looking to enter this field.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines that collect, process, and store data. They may use libraries like AutoGluon to automate the process of model training and selection. This course provides a good foundation in using AutoGluon, which can be useful for those looking to enter this field.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. They may use libraries like AutoGluon to automate the process of model training and selection. This course provides a good foundation in using AutoGluon, which can be useful for those looking to enter this field.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior. They may use libraries like AutoGluon to automate the process of model training and selection. This course provides a good foundation in using AutoGluon, which can be useful for those looking to enter this field.
Product Manager
Product Manager
Product Managers define and manage the development of new products and features. They may use libraries like AutoGluon to evaluate the performance of new models. This course provides a good introduction to using AutoGluon, which can be useful for those looking to enter this field.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve problems in business and industry. They may use libraries like AutoGluon to automate the process of model training and selection. This course provides a good foundation in using AutoGluon, which can be useful for those looking to enter this field.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze data in the field of biology and medicine. They may use libraries like AutoGluon to automate the process of model training and selection. This course provides a good foundation in using AutoGluon, which can be useful for those looking to enter this field.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. They may use libraries like AutoGluon to automate the process of model training and selection. This course provides a good foundation in using AutoGluon, which can be useful for those looking to enter this field.
Risk Manager
Risk Manager
Risk Managers identify and manage risks. They may use libraries like AutoGluon to automate the process of model training and selection. This course provides a good foundation in using AutoGluon, which can be useful for those looking to enter this field.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. They may use libraries like AutoGluon to automate the process of model training and selection. This course provides a good foundation in using AutoGluon, which can be useful for those looking to enter this field.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They may use libraries like AutoGluon to automate the process of model training and selection. This course provides a good foundation in using AutoGluon, which can be useful for those looking to enter this field.
Data Analyst
Data Analyst
Data Analysts use data to make informed decisions. They may use libraries like AutoGluon to automate the process of model training and selection. This course provides a good foundation in using AutoGluon, which can be useful for those looking to enter this field.
For more career information including salaries, visit:
OpenCourser.com/course/6d8hpe/auto
Reading list
We've selected nine 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
Auto Machine Learning (AutoML) Using AutoGluon .
This practical guide provides step-by-step instructions for implementing machine learning algorithms using popular Python libraries.
Save
Comprehensive overview of deep learning, covering both理論 and practical aspects.
Provides a practical introduction to machine learning using the R programming language.
Provides a practical introduction to machine learning using the Python programming language.
Provides an overview of data science concepts, techniques, and applications.
Provides a comprehensive overview of machine learning algorithms and techniques.
Provides an overview of machine learning algorithms and techniques for big data applications.
Provides a concise overview of machine learning algorithms.
Provides a concise and accessible overview of machine learning concepts and algorithms.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6d8hpe/auto
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Develops machine learning modeling skills using a few lines of code, which is highly relevant to industry
Covers Amazon Web Service AutoPilot, which is widely used in industry
Enhances portfolio projects for job interviews
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