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Ryan Ahmed
Hello everyone and welcome to this new hands-on project on Machine Learning hyperparameters optimization. In this project, we will optimize machine learning regression models parameters using several techniques such as grid search, random search and Bayesian optimization. Hyperparameter optimization is a key step in developing machine learning models and it works by fine tuning ML models so they can optimally perform on a given dataset.
Register for this course and see more details by visiting:
OpenCourser.com/course/z11nej/ml
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Syllabus
Project Overview
Hello everyone and welcome to this new hands-on project on Machine Learning hyperparameters optimization.
Hyperparameter optimization is a key step in developing machine learning models and it works by fine tuning Machine Learning models so they can optimally perform on a given dataset. In this project, we will optimize machine learning regression models parameters using several techniques such as grid search, random search and Bayesian optimization.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Emphasizes fine-tuning machine learning models for better performance on given datasets
Covers techniques like grid search, random search, and Bayesian optimization, which are industry standards
Provides hands-on experience with optimizing machine learning regression models
Designed for learners with a basic understanding of machine learning concepts
Assumes familiarity with programming and machine learning libraries
Save this course
Save ML Parameters Optimization: GridSearch, Bayesian, Random 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 ML Parameters Optimization: GridSearch, Bayesian, Random with these
activities:
Review fundamentals of supervised learning algorithms
Show steps
Ensure a solid understanding of core concepts and algorithms underlying supervised learning.
Browse courses on
Supervised Learning
Show steps
-
Revisit materials from prior coursework or online resources on supervised learning.
-
Review the concepts of linear regression, logistic regression, and decision trees.
-
Practice solving model selection problems.
Attend a workshop on advanced hyperparameter optimization techniques
Show steps
Gain practical insights and learn from industry experts on cutting-edge optimization techniques.
Show steps
-
Research and identify relevant workshops.
-
Register and attend the workshop.
-
Actively participate in discussions and hands-on exercises.
Explore Coursera's ML Deep Dive Modules
Show steps
Solidify your comprehension of course concepts by practicing on Coursera's curated machine learning modules.
Browse courses on
Machine Learning
Show steps
-
Enroll in one or more of the modules offered by Coursera on Machine Learning Regression.
-
Go through the video lectures, complete practice exercises, and engage in discussion forums.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve hyperparameter optimization exercises
Show steps
Develop proficiency in applying hyperparameter optimization techniques.
Show steps
-
Work through exercises on grid search, random search, and Bayesian optimization.
-
Analyze the results of different hyperparameter combinations.
-
Experiment with different optimization strategies.
Participate in group discussions on hyperparameter optimization
Show steps
Engage with peers and exchange ideas to enhance understanding and problem-solving skills.
Show steps
-
Join or create a study group focused on hyperparameter optimization.
-
Facilitate discussions on optimizing models for various datasets.
-
Share experiences and insights with group members.
Participate in a machine learning competition focused on hyperparameter optimization
Show steps
Apply skills in a competitive environment to showcase understanding and push the boundaries of knowledge.
Show steps
-
Identify and register for a relevant competition.
-
Develop and implement a hyperparameter optimization strategy.
-
Monitor and evaluate performance against other competitors.
-
Analyze results and identify areas for improvement.
Contribute to an open-source hyperparameter optimization project
Show steps
Contribute to the advancement of hyperparameter optimization by collaborating with the open-source community.
Show steps
-
Identify an open-source project focused on hyperparameter optimization.
-
Review the project's documentation and codebase.
-
Identify areas where contributions can be made.
-
Submit pull requests with proposed improvements or new features.
Explore advanced hyperparameter optimization methods
Show steps
Expand knowledge and explore cutting-edge techniques for hyperparameter optimization.
Show steps
-
Follow online tutorials or courses on topics such as evolutionary algorithms and reinforcement learning for hyperparameter optimization.
-
Implement and test advanced optimization algorithms on real-world datasets.
-
Evaluate the performance of different optimization strategies.
Develop a custom hyperparameter optimization tool
Show steps
Apply knowledge and consolidate understanding by building a practical tool for hyperparameter optimization.
Show steps
-
Design the architecture and functionality of the tool.
-
Implement the tool using a programming language of choice.
-
Test and validate the tool's performance.
-
Document and share the tool with the community.
Review fundamentals of supervised learning algorithms
Show steps
Ensure a solid understanding of core concepts and algorithms underlying supervised learning.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Revisit materials from prior coursework or online resources on supervised learning.
- Review the concepts of linear regression, logistic regression, and decision trees.
- Practice solving model selection problems.
Attend a workshop on advanced hyperparameter optimization techniques
Show steps
Gain practical insights and learn from industry experts on cutting-edge optimization techniques.
Show steps
Show steps
Show steps
- Research and identify relevant workshops.
- Register and attend the workshop.
- Actively participate in discussions and hands-on exercises.
Explore Coursera's ML Deep Dive Modules
Show steps
Solidify your comprehension of course concepts by practicing on Coursera's curated machine learning modules.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Enroll in one or more of the modules offered by Coursera on Machine Learning Regression.
- Go through the video lectures, complete practice exercises, and engage in discussion forums.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve hyperparameter optimization exercises
Show steps
Develop proficiency in applying hyperparameter optimization techniques.
Show steps
Show steps
Show steps
- Work through exercises on grid search, random search, and Bayesian optimization.
- Analyze the results of different hyperparameter combinations.
- Experiment with different optimization strategies.
Participate in group discussions on hyperparameter optimization
Show steps
Engage with peers and exchange ideas to enhance understanding and problem-solving skills.
Show steps
Show steps
Show steps
- Join or create a study group focused on hyperparameter optimization.
- Facilitate discussions on optimizing models for various datasets.
- Share experiences and insights with group members.
Participate in a machine learning competition focused on hyperparameter optimization
Show steps
Apply skills in a competitive environment to showcase understanding and push the boundaries of knowledge.
Show steps
Show steps
Show steps
- Identify and register for a relevant competition.
- Develop and implement a hyperparameter optimization strategy.
- Monitor and evaluate performance against other competitors.
- Analyze results and identify areas for improvement.
Contribute to an open-source hyperparameter optimization project
Show steps
Contribute to the advancement of hyperparameter optimization by collaborating with the open-source community.
Show steps
Show steps
Show steps
- Identify an open-source project focused on hyperparameter optimization.
- Review the project's documentation and codebase.
- Identify areas where contributions can be made.
- Submit pull requests with proposed improvements or new features.
Explore advanced hyperparameter optimization methods
Show steps
Expand knowledge and explore cutting-edge techniques for hyperparameter optimization.
Show steps
Show steps
Show steps
- Follow online tutorials or courses on topics such as evolutionary algorithms and reinforcement learning for hyperparameter optimization.
- Implement and test advanced optimization algorithms on real-world datasets.
- Evaluate the performance of different optimization strategies.
Develop a custom hyperparameter optimization tool
Show steps
Apply knowledge and consolidate understanding by building a practical tool for hyperparameter optimization.
Show steps
Show steps
Show steps
- Design the architecture and functionality of the tool.
- Implement the tool using a programming language of choice.
- Test and validate the tool's performance.
- Document and share the tool with the community.
Career center
Learners who complete ML Parameters Optimization: GridSearch, Bayesian, Random will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning systems. They work closely with Data Scientists to ensure that models are performing optimally. This course can help you develop the skills you need to become a successful Machine Learning Engineer. You will learn how to optimize machine learning models using grid search, random search, and Bayesian optimization. This knowledge will help you build and deploy systems that perform optimally on a given dataset.
Data Scientist
Data Scientist
Data Scientists are responsible for developing, deploying, and maintaining machine learning models. They use their knowledge of statistics, computer science, and business to solve complex problems. This course can help you develop the skills you need to become a successful Data Scientist. You will learn how to optimize machine learning models using grid search, random search, and Bayesian optimization. This knowledge will help you build and deploy models that perform optimally on a given dataset.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They use this information to make investment decisions. This course can help you develop the skills you need to become a successful Quantitative Analyst. You will learn how to optimize machine learning models using grid search, random search, and Bayesian optimization. This knowledge will help you build and deploy models that perform optimally on a given dataset.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex problems in business and industry. They work with businesses to improve their efficiency and profitability. This course can help you develop the skills you need to become a successful Operations Research Analyst. You will learn how to optimize machine learning models using grid search, random search, and Bayesian optimization. This knowledge will help you build and deploy models that perform optimally on a given dataset.
Business Analyst
Business Analyst
Business Analysts use data to analyze business processes and make recommendations for improvement. They work with businesses to identify opportunities for growth and efficiency. This course can help you develop the skills you need to become a successful Business Analyst. You will learn how to optimize machine learning models using grid search, random search, and Bayesian optimization. This knowledge will help you build and deploy models that perform optimally on a given dataset.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. They work with businesses to identify opportunities for growth and efficiency. This course can help you develop the skills you need to become a successful Data Analyst. You will learn how to optimize machine learning models using grid search, random search, and Bayesian optimization. This knowledge will help you build and deploy models that perform optimally on a given dataset.
Statistician
Statistician
Statisticians use data to analyze and draw conclusions. They work with businesses and organizations to make informed decisions. This course can help you develop the skills you need to become a successful Statistician. You will learn how to optimize machine learning models using grid search, random search, and Bayesian optimization. This knowledge will help you build and deploy models that perform optimally on a given dataset.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with businesses and organizations to create software that meets their needs. This course can help you develop the skills you need to become a successful Software Engineer. You will learn how to optimize machine learning models using grid search, random search, and Bayesian optimization. This knowledge will help you build and deploy software that performs optimally.
Computer Scientist
Computer Scientist
Computer Scientists research the theory and application of computation. They work to develop new and innovative ways to solve problems. This course can help you develop the skills you need to become a successful Computer Scientist. You will learn how to optimize machine learning models using grid search, random search, and Bayesian optimization. This knowledge will help you develop new and innovative ways to solve problems.
Mathematician
Mathematician
Mathematicians develop and apply mathematical theories and techniques to solve problems in science, engineering, and other fields. This course can help you develop the skills you need to become a successful Mathematician. You will learn how to optimize machine learning models using grid search, random search, and Bayesian optimization. This knowledge will help you develop new and innovative ways to solve problems.
Physicist
Physicist
Physicists study the fundamental laws of nature. They work to develop new theories and technologies to explain the universe. This course can help you develop the skills you need to become a successful Physicist. You will learn how to optimize machine learning models using grid search, random search, and Bayesian optimization. This knowledge will help you develop new and innovative ways to solve problems.
Chemist
Chemist
Chemists study the composition and behaviour of matter. They work to develop new materials and technologies. This course can help you develop the skills you need to become a successful Chemist. You will learn how to optimize machine learning models using grid search, random search, and Bayesian optimization. This knowledge will help you develop new and innovative ways to solve problems.
Biologist
Biologist
Biologists study the structure and function of living organisms. They work to develop new medicines and technologies to improve human health. This course can help you develop the skills you need to become a successful Biologist. You will learn how to optimize machine learning models using grid search, random search, and Bayesian optimization. This knowledge will help you develop new and innovative ways to solve problems.
Geologist
Geologist
Geologists study the Earth's structure, composition, and history. They work to develop new technologies to extract resources from the Earth. This course can help you develop the skills you need to become a successful Geologist. You will learn how to optimize machine learning models using grid search, random search, and Bayesian optimization. This knowledge will help you develop new and innovative ways to solve problems.
Environmental Scientist
Environmental Scientist
Environmental Scientists study the environment and its impact on human health. They work to develop new technologies to protect the environment. This course can help you develop the skills you need to become a successful Environmental Scientist. You will learn how to optimize machine learning models using grid search, random search, and Bayesian optimization. This knowledge will help you develop new and innovative ways to solve problems.
For more career information including salaries, visit:
OpenCourser.com/course/z11nej/ml
Reading list
We've selected 12 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
ML Parameters Optimization: GridSearch, Bayesian, Random.
Save
This textbook provides a comprehensive overview of the mathematical foundations of ML. It covers a wide range of topics, from linear algebra to probability theory to optimization. Provides a theoretical foundation for ML algorithms and models.
This textbook provides a comprehensive overview of the field of pattern recognition and ML. It covers a wide range of topics, from supervised learning to unsupervised learning to reinforcement learning. It's widely used as a textbook at the academic level.
Save
This textbook provides a comprehensive overview of the field of deep learning. It covers a wide range of topics, from convolutional neural networks to recurrent neural networks to generative adversarial networks. It valuable resource that aids in understanding the theory and practice of deep learning.
This textbook provides a comprehensive overview of the statistical foundations of ML. It covers a wide range of topics, from linear regression to logistic regression to decision trees. It's a valuable resource that aids in selecting algorithms and models for ML tasks.
This textbook provides a comprehensive overview of the probabilistic foundations of ML. It covers a wide range of topics, from Bayesian inference to graphical models to deep learning. It's a valuable resource that aids in learning advanced probabilistic ML topics.
This textbook provides a comprehensive overview of the field of reinforcement learning. It covers a wide range of topics, from Markov decision processes to value function approximation to policy optimization. It valuable resource for anyone who wants to learn more about reinforcement learning.
Provides a practical guide to ML using the R programming language. It covers a wide range of topics, from data preprocessing to model evaluation. It is an excellent resource for practitioners who want to use R for ML projects.
Provides a comprehensive overview of the types of algorithms used in ML. It covers a wide range of topics, from linear regression to decision trees to neural networks. It is an excellent resource for students and practitioners who want to learn more about the algorithms that power ML.
This textbook provides a clear and comprehensive introduction to the field of machine learning. It covers a wide range of topics, from supervised learning to unsupervised learning to reinforcement learning. It is valuable to anyone who wants to learn the basics of ML.
Provides a practical guide to ML for non-experts. It covers a wide range of topics, from data preprocessing to model evaluation. It valuable resource for anyone who wants to learn more about ML without getting bogged down in the details.
Provides a concise and accessible introduction to ML. It covers a wide range of topics, from supervised learning to unsupervised learning to reinforcement learning. It valuable resource for anyone who wants to learn the basics of ML.
Provides a gentle introduction to the field of ML. It covers a wide range of topics, from supervised learning to unsupervised learning to reinforcement learning. It valuable resource for beginners who want to learn more about ML.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/z11nej/ml
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Emphasizes fine-tuning machine learning models for better performance on given datasets
Covers techniques like grid search, random search, and Bayesian optimization, which are industry standards
Provides hands-on experience with optimizing machine learning regression models
Designed for learners with a basic understanding of machine learning concepts
Assumes familiarity with programming and machine learning libraries
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