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Daniel Stern
Machine learning models are of critical business importance, and hyperparameters allow us to converge on more accurate models faster. This course will teach you how to understand and optimize hyperparameters by tuning them.
Machine learning (ML) models are extremely powerful, and many enterprise companies use them extensively. But how can you get the most performance out of your models without investing excessive time or resources in the training process?
In this course, Optimize Model Training with Hyperparameter Tuning, you'll learn to optimize your model performance by tuning hyperparameters.
Read more
Machine learning models are of critical business importance, and hyperparameters allow us to converge on more accurate models faster. This course will teach you how to understand and optimize hyperparameters by tuning them.
Machine learning (ML) models are extremely powerful, and many enterprise companies use them extensively. But how can you get the most performance out of your models without investing excessive time or resources in the training process?
In this course, Optimize Model Training with Hyperparameter Tuning, you'll learn to optimize your model performance by tuning hyperparameters.
Read more
Machine learning models are of critical business importance, and hyperparameters allow us to converge on more accurate models faster. This course will teach you how to understand and optimize hyperparameters by tuning them.
Machine learning (ML) models are extremely powerful, and many enterprise companies use them extensively. But how can you get the most performance out of your models without investing excessive time or resources in the training process?
In this course, Optimize Model Training with Hyperparameter Tuning, you'll learn to optimize your model performance by tuning hyperparameters.
First, you’ll explore the basics of hyperparameters - what they are, how they are used, the different categories of hyperparameter, and to which model types they apply.
Next, you’ll discover how to tune hyperparameters - including different techniques, softwares, automated and manual tuning, and the outcomes of tuning hyperparameters on ML business goals.
Finally, you'll learn the difference between manual and automated tuning.
When you’re finished with this course, you’ll have the skills and knowledge of tuning hyperparameters needed to effectively optimize ML models.
Register for this course and see more details by visiting:
OpenCourser.com/course/62cw49/optimize
What's inside
Syllabus
Course Overview
Understanding Hyperparameters
Tuning Hyperparameters
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills needed for optimizing model training, which is core for applying machine learning in businesses
Taught by Daniel Stern, who are recognized for their expertise in machine learning and hyperparameter tuning
Examines automated and manual techniques for tuning hyperparameters, allowing learners to choose the best method for their needs
Explores the different categories and applications of hyperparameters, providing a comprehensive understanding of their role in machine learning models
Teaches skills that are highly relevant to the industry, enabling learners to enhance their professional capabilities in machine learning
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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 Optimize Model Training with Hyperparameter Tuning with these
activities:
Review the basics of machine learning
Show steps
Refresh your knowledge of the fundamentals to strengthen your understanding of hyperparameter tuning.
Browse courses on
Machine Learning Basics
Show steps
-
Revisit your notes or textbooks from previous machine learning courses.
-
Go through online tutorials or resources that cover the basics of machine learning.
-
Attend a refresher workshop or webinar on machine learning.
Review 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow', 2nd Edition, by Aurélien Géron
Show steps
Enhance your knowledge by reviewing a comprehensive resource on machine learning.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read the book's chapters on hyperparameter tuning.
-
Work through the exercises and examples provided in the book.
Review Foundational Machine Learning Concepts
Show steps
Strengthen your foundation in ML concepts to fully grasp hyperparameter tuning techniques.
Browse courses on
Machine Learning Fundamentals
Show steps
-
Revisit textbooks or online resources on foundational ML concepts.
-
Complete practice exercises to reinforce your understanding.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Review Intermediate Python Concepts
Show steps
Refreshes on and consolidates Intermediate Python Concepts
Browse courses on
Python
Show steps
-
Review functions, classes, and object-oriented programming concepts.
-
Practice writing and executing Python code to reinforce understanding.
Follow tutorials on hyperparameter tuning
Show steps
Supplement your learning by seeking additional guidance and resources.
Show steps
-
Find tutorials that cover the basics of hyperparameter tuning.
-
Follow the steps outlined in the tutorials.
-
Apply the techniques you learn to your own machine learning projects.
Assist Peer Learners in the Course Discussion Forum
Show steps
Promotes knowledge sharing, collaboration, and deepens understanding through peer interaction.
Browse courses on
Collaboration
Show steps
-
Actively participate in the course discussion forum.
-
Provide helpful answers and insights to questions posed by peers.
-
Engage in respectful and constructive discussions.
Review 'Machine Learning' by Tom Mitchell
Show steps
Reinforce basic machine learning concepts covered in the course through reading Tom Mitchell's authoritative work.
View
Machine Learning (McGraw-Hill International...
on Amazon
Show steps
-
Read the first five chapters of the book
-
Summarize each chapter's key concepts
-
Identify how each chapter relates to the course material
Seek Out Mentorship
Show steps
Discuss hyperparameter tuning strategies with experienced practitioners to gain insights and best practices.
Show steps
-
Identify potential mentors in your network or through online platforms.
-
Reach out to mentors and request guidance on hyperparameter tuning.
-
Schedule regular meetings or connect through online platforms to discuss progress.
Complete the 'Hyperparameter Tuning with Optuna' Tutorial
Show steps
Provides practical hands-on experience with hyperparameter tuning using Optuna.
Browse courses on
Hyperparameter Tuning
Show steps
-
Follow the 'Hyperparameter Tuning with Optuna' tutorial step-by-step.
-
Experiment with different hyperparameters and observe their impact on model performance.
Solve the 'Hyperparameter Tuning Practice Problems'
Show steps
Sharpens analytical and problem-solving skills in the context of hyperparameter tuning.
Browse courses on
Hyperparameter Tuning
Show steps
-
Attempt the 'Hyperparameter Tuning Practice Problems' provided.
-
Analyze and interpret the results to identify optimal hyperparameters.
Explore Advanced Hyperparameter Tuning Techniques
Show steps
Enhance your understanding of advanced hyperparameter tuning techniques to optimize model performance.
Browse courses on
Bayesian Optimization
Show steps
-
Identify relevant advanced techniques, such as Bayesian optimization.
-
Follow online tutorials or documentation to learn about the techniques.
-
Apply these techniques to your own ML projects to experiment and gain practical experience.
Build a machine learning model and tune its hyperparameters
Show steps
Apply your knowledge by working on a hands-on project.
Show steps
-
Choose a machine learning task to work on.
-
Build a baseline machine learning model.
-
Tune the hyperparameters of your model using the techniques you learned in the course.
-
Evaluate the performance of your tuned model.
Design a Hyperparameter Tuning Strategy for a Real-World Dataset
Show steps
Encourages critical thinking and practical application of hyperparameter tuning.
Browse courses on
Hyperparameter Tuning
Show steps
-
Select a real-world dataset and define a specific machine learning task.
-
Develop a strategy for selecting and tuning hyperparameters.
-
Implement the strategy and evaluate the results.
Write a blog post or article on hyperparameter tuning
Show steps
Showcase your learning by creating a deliverable that demonstrates your understanding.
Show steps
-
Choose a topic related to hyperparameter tuning that you want to write about.
-
Research the topic and gather information from credible sources.
-
Organize your thoughts and create an outline for your blog post or article.
-
Write your blog post or article, ensuring that it is well-written, informative, and engaging.
-
Publish your blog post or article on a reputable platform.
Contribute to Open-Source Hyperparameter Tuning Projects
Show steps
Deepen your understanding and contribute to the advancement of hyperparameter tuning by participating in open-source projects.
Show steps
-
Identify open-source projects related to hyperparameter tuning.
-
Review the project documentation and codebase.
-
Contribute code, report bugs, or suggest improvements to the project.
Review the basics of machine learning
Show steps
Refresh your knowledge of the fundamentals to strengthen your understanding of hyperparameter tuning.
Browse courses on
Machine Learning Basics
Show steps
Show steps
Show steps
- Revisit your notes or textbooks from previous machine learning courses.
- Go through online tutorials or resources that cover the basics of machine learning.
- Attend a refresher workshop or webinar on machine learning.
Review 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow', 2nd Edition, by Aurélien Géron
Show steps
Enhance your knowledge by reviewing a comprehensive resource on machine learning.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read the book's chapters on hyperparameter tuning.
- Work through the exercises and examples provided in the book.
Review Foundational Machine Learning Concepts
Show steps
Strengthen your foundation in ML concepts to fully grasp hyperparameter tuning techniques.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Revisit textbooks or online resources on foundational ML concepts.
- Complete practice exercises to reinforce your understanding.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Review Intermediate Python Concepts
Show steps
Refreshes on and consolidates Intermediate Python Concepts
Browse courses on
Python
Show steps
Show steps
Show steps
- Review functions, classes, and object-oriented programming concepts.
- Practice writing and executing Python code to reinforce understanding.
Follow tutorials on hyperparameter tuning
Show steps
Supplement your learning by seeking additional guidance and resources.
Show steps
Show steps
Show steps
- Find tutorials that cover the basics of hyperparameter tuning.
- Follow the steps outlined in the tutorials.
- Apply the techniques you learn to your own machine learning projects.
Assist Peer Learners in the Course Discussion Forum
Show steps
Promotes knowledge sharing, collaboration, and deepens understanding through peer interaction.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Actively participate in the course discussion forum.
- Provide helpful answers and insights to questions posed by peers.
- Engage in respectful and constructive discussions.
Review 'Machine Learning' by Tom Mitchell
Show steps
Reinforce basic machine learning concepts covered in the course through reading Tom Mitchell's authoritative work.
View
Machine Learning (McGraw-Hill International...
on Amazon
Show steps
Show steps
Show steps
- Read the first five chapters of the book
- Summarize each chapter's key concepts
- Identify how each chapter relates to the course material
Seek Out Mentorship
Show steps
Discuss hyperparameter tuning strategies with experienced practitioners to gain insights and best practices.
Show steps
Show steps
Show steps
- Identify potential mentors in your network or through online platforms.
- Reach out to mentors and request guidance on hyperparameter tuning.
- Schedule regular meetings or connect through online platforms to discuss progress.
Complete the 'Hyperparameter Tuning with Optuna' Tutorial
Show steps
Provides practical hands-on experience with hyperparameter tuning using Optuna.
Browse courses on
Hyperparameter Tuning
Show steps
Show steps
Show steps
- Follow the 'Hyperparameter Tuning with Optuna' tutorial step-by-step.
- Experiment with different hyperparameters and observe their impact on model performance.
Solve the 'Hyperparameter Tuning Practice Problems'
Show steps
Sharpens analytical and problem-solving skills in the context of hyperparameter tuning.
Browse courses on
Hyperparameter Tuning
Show steps
Show steps
Show steps
- Attempt the 'Hyperparameter Tuning Practice Problems' provided.
- Analyze and interpret the results to identify optimal hyperparameters.
Explore Advanced Hyperparameter Tuning Techniques
Show steps
Enhance your understanding of advanced hyperparameter tuning techniques to optimize model performance.
Browse courses on
Bayesian Optimization
Show steps
Show steps
Show steps
- Identify relevant advanced techniques, such as Bayesian optimization.
- Follow online tutorials or documentation to learn about the techniques.
- Apply these techniques to your own ML projects to experiment and gain practical experience.
Build a machine learning model and tune its hyperparameters
Show steps
Apply your knowledge by working on a hands-on project.
Show steps
Show steps
Show steps
- Choose a machine learning task to work on.
- Build a baseline machine learning model.
- Tune the hyperparameters of your model using the techniques you learned in the course.
- Evaluate the performance of your tuned model.
Design a Hyperparameter Tuning Strategy for a Real-World Dataset
Show steps
Encourages critical thinking and practical application of hyperparameter tuning.
Browse courses on
Hyperparameter Tuning
Show steps
Show steps
Show steps
- Select a real-world dataset and define a specific machine learning task.
- Develop a strategy for selecting and tuning hyperparameters.
- Implement the strategy and evaluate the results.
Write a blog post or article on hyperparameter tuning
Show steps
Showcase your learning by creating a deliverable that demonstrates your understanding.
Show steps
Show steps
Show steps
- Choose a topic related to hyperparameter tuning that you want to write about.
- Research the topic and gather information from credible sources.
- Organize your thoughts and create an outline for your blog post or article.
- Write your blog post or article, ensuring that it is well-written, informative, and engaging.
- Publish your blog post or article on a reputable platform.
Contribute to Open-Source Hyperparameter Tuning Projects
Show steps
Deepen your understanding and contribute to the advancement of hyperparameter tuning by participating in open-source projects.
Show steps
Show steps
Show steps
- Identify open-source projects related to hyperparameter tuning.
- Review the project documentation and codebase.
- Contribute code, report bugs, or suggest improvements to the project.
Career center
Learners who complete Optimize Model Training with Hyperparameter Tuning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. Optimize Model Training with Hyperparameter Tuning will provide you with the knowledge and skills needed to develop more powerful and efficient machine learning models. This course will teach you how to understand and tune hyperparameters, which are critical for achieving optimal model performance. By taking this course, you can gain a competitive advantage in the field of machine learning research.
Statistician
Statistician
Statisticians use statistical methods to analyze data and make predictions. Optimize Model Training with Hyperparameter Tuning will provide you with the knowledge and skills needed to develop and apply statistical models to real-world problems. This course will teach you how to understand and tune hyperparameters, which are critical for achieving optimal model performance. By taking this course, you can gain a competitive advantage in the field of statistics.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will design, develop, and deploy machine learning models to solve complex business problems. Optimize Model Training with Hyperparameter Tuning will provide you with the knowledge and skills needed to optimize the performance of your models, resulting in more accurate and reliable results. This course will teach you how to understand and tune hyperparameters, which are critical for achieving optimal model performance. By taking this course, you can gain a competitive advantage in the field of machine learning engineering.
Quantitative Analyst
Quantitative Analyst
As a Quantitative Analyst, you will use mathematical and statistical models to analyze data and make predictions. Optimize Model Training with Hyperparameter Tuning will provide you with the knowledge and skills needed to optimize the performance of your models, resulting in more accurate and reliable results. This course will teach you how to understand and tune hyperparameters, which are critical for achieving optimal model performance. By taking this course, you can gain a competitive advantage in the field of quantitative analysis.
Data Scientist
Data Scientist
Data Scientists use machine learning algorithms and statistical models to extract insights from data, helping businesses make informed decisions. Optimize Model Training with Hyperparameter Tuning will provide you with the skills needed to optimize the performance of your models, resulting in more accurate and reliable results. This course will teach you how to understand and tune hyperparameters, which are critical for achieving optimal model performance. By taking this course, you can gain a competitive advantage in the field of data science.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex business problems. Optimize Model Training with Hyperparameter Tuning will provide you with the knowledge and skills needed to develop and apply optimization models to real-world problems. This course will teach you how to understand and tune hyperparameters, which are critical for achieving optimal model performance. By taking this course, you can gain a competitive advantage in the field of operations research.
Software Engineer
Software Engineer
Optimize Model Training with Hyperparameter Tuning may be useful for Software Engineers who are working on developing machine learning models. This course will teach you how to understand and tune hyperparameters, which are critical for achieving optimal model performance. By taking this course, you can gain a competitive advantage in the field of software engineering and become more valuable to potential employers.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. Optimize Model Training with Hyperparameter Tuning may be useful for Actuaries who are using machine learning models to assess risk and uncertainty. This course can help you to understand the process of tuning hyperparameters, which is critical for achieving optimal model performance. By taking this course, you can gain a competitive advantage in the field of actuarial science.
Risk Manager
Risk Manager
Risk Managers identify, assess, and manage risks for businesses. Optimize Model Training with Hyperparameter Tuning may be useful for Risk Managers who are using machine learning models to assess and manage risks. This course can help you to understand the process of tuning hyperparameters, which is critical for achieving optimal model performance. By taking this course, you can gain a competitive advantage in the field of risk management.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment decisions. Optimize Model Training with Hyperparameter Tuning may be useful for Financial Analysts who are using machine learning models to make investment decisions. This course can help you to understand the process of tuning hyperparameters, which is critical for achieving optimal model performance. By taking this course, you can gain a competitive advantage in the field of financial analysis.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products and services. Optimize Model Training with Hyperparameter Tuning may be useful for Product Managers who are working with machine learning models. This course can help you to understand the process of tuning hyperparameters, which is critical for achieving optimal model performance. By taking this course, you can gain a competitive advantage in the field of product management.
Business Analyst
Business Analyst
Business Analysts use data and analytics to help businesses make informed decisions. Optimize Model Training with Hyperparameter Tuning may be useful for Business Analysts who are working with machine learning models. This course can help you to understand the process of tuning hyperparameters, which is critical for achieving optimal model performance. By taking this course, you can gain a competitive advantage in the field of business analysis.
Consultant
Consultant
Consultants help businesses solve complex problems and make informed decisions. Optimize Model Training with Hyperparameter Tuning may be useful for Consultants who are working with clients who are using machine learning models. This course can help you to understand the process of tuning hyperparameters, which is critical for achieving optimal model performance. By taking this course, you can gain a competitive advantage in the field of consulting.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain the infrastructure that stores and processes data. Optimize Model Training with Hyperparameter Tuning may be useful for Data Engineers who are working with machine learning models. This course can help you to understand the process of tuning hyperparameters, which is critical for achieving optimal model performance. By taking this course, you can gain a competitive advantage in the field of data engineering.
Data Analyst
Data Analyst
Data Analysts use data and analytics to help businesses make informed decisions. Optimize Model Training with Hyperparameter Tuning may be useful for Data Analysts who are working with machine learning models. This course can help you to understand the process of tuning hyperparameters, which is critical for achieving optimal model performance. By taking this course, you can gain a competitive advantage in the field of data analysis.
For more career information including salaries, visit:
OpenCourser.com/course/62cw49/optimize
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
Optimize Model Training with Hyperparameter Tuning.
Save
Covers a wide range of optimization techniques used in machine learning, including hyperparameter tuning, and is useful for a deeper understanding of the underlying algorithms.
While focused on automated machine learning, it provides valuable insights into hyperparameter tuning and optimization.
Covers Bayesian optimization techniques used in hyperparameter tuning, providing a good theoretical foundation.
Provides a practical guide to machine learning using popular libraries such as Scikit-Learn, Keras, and TensorFlow. It includes a chapter on hyperparameter tuning, making it a useful resource for those who want to apply these techniques in practice.
Save
Provides a comprehensive overview of deep learning, including a chapter on hyperparameter tuning. It useful resource for those who want to understand the theoretical foundations of hyperparameter tuning.
Provides a Bayesian perspective on machine learning, including a chapter on hyperparameter tuning. It valuable resource for those who want to understand the theoretical foundations of hyperparameter tuning.
Save
Provides a comprehensive overview of machine learning, including a chapter on hyperparameter tuning. It useful resource for those who want to understand the theoretical foundations of hyperparameter tuning.
Provides a practical guide to machine learning using Python. It includes a chapter on hyperparameter tuning, making it a useful resource for those who want to apply these techniques in practice.
Provides a comprehensive overview of artificial intelligence, including a chapter on hyperparameter tuning. It useful resource for those who want to understand the theoretical foundations of hyperparameter tuning.
Provides a practical guide to machine learning for software engineers. It includes a chapter on hyperparameter tuning, making it a useful resource for those who want to apply these techniques in practice.
Provides a comprehensive overview of reinforcement learning, including a chapter on hyperparameter tuning. It useful resource for those who want to understand the theoretical foundations of hyperparameter tuning in the context of reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/62cw49/optimize
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Time
27 hours
Level
Introductory
Via
Pluralsight
Instructor
Daniel Stern
Language
English
Good to know
Develops skills needed for optimizing model training, which is core for applying machine learning in businesses
Taught by Daniel Stern, who are recognized for their expertise in machine learning and hyperparameter tuning
Examines automated and manual techniques for tuning hyperparameters, allowing learners to choose the best method for their needs
Explores the different categories and applications of hyperparameters, providing a comprehensive understanding of their role in machine learning models
Teaches skills that are highly relevant to the industry, enabling learners to enhance their professional capabilities in machine learning
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