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Tim Warner
This course is intended for data science practitioners who work with Azure Machine Learning Service and who seek to improve their ML model accuracy, efficiency, and explainability.
Read more
This course is intended for data science practitioners who work with Azure Machine Learning Service and who seek to improve their ML model accuracy, efficiency, and explainability.
Read more
This course is intended for data science practitioners who work with Azure Machine Learning Service and who seek to improve their ML model accuracy, efficiency, and explainability.
Data science and machine learning professionals work tirelessly to improve the quality of their ML models. In this course, Evaluating Model Effectiveness in Microsoft Azure, you will learn how to use Azure Machine Learning Studio to improve your models. First, you will learn how to evaluate model effectiveness in Azure. Next, you will discover how to improve model performance by eliminating overfitting and implementing ensembling. Finally, you will explore how to assess ML model interpretability. When you are finished with this course, you will have the skills and knowledge of Azure Machine Learning needed to ensure your ML models are consistent, accurate, and explainable.
Register for this course and see more details by visiting:
OpenCourser.com/course/9jp885/evaluating
What's inside
Syllabus
Course Overview
Evaluating Model Effectiveness
Improving Model Performance
Assessing Model Explainability
Read more
Syllabus
Course Overview
Evaluating Model Effectiveness
Improving Model Performance
Assessing Model Explainability
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Delves into assessing ML model interpretability, which is imperative for understanding and explaining model predictions
Utilizes Azure Machine Learning Studio, a popular platform for training and deploying ML models
Provides techniques to enhance model performance, such as eliminating overfitting and implementing ensembling
Taught by Tim Warner, an expert in data science and machine learning
Appropriate for data science professionals seeking to improve their ML model effectiveness
Requires prior knowledge of Azure Machine Learning Service, which may limit accessibility for beginners
Save this course
Save Evaluating Model Effectiveness in Microsoft Azure 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 Evaluating Model Effectiveness in Microsoft Azure with these
activities:
Identify industry experts or experienced practitioners for mentorship
Show steps
Enhance your learning experience by seeking guidance and support from experienced professionals in the field of ML.
Browse courses on
Mentorship
Show steps
-
Research potential mentors through online platforms, industry events, or professional networks.
-
Reach out to individuals who align with your career goals and interests.
-
Articulate your mentorship needs and goals clearly.
Review of basic Azure features and concepts
Show steps
Sharpen your existing knowledge of Azure to better grasp the concepts and techniques taught in this course.
Browse courses on
Azure
Show steps
-
Go over the Azure portal interface and fundamental concepts such as resource groups, storage accounts, and virtual networks.
-
Review Azure documentation and tutorials to refresh your memory on essential Azure services and tools.
Complete hands-on tutorials on Azure Machine Learning
Show steps
Reinforce your understanding of Azure Machine Learning by following guided tutorials that provide practical, hands-on experience.
Browse courses on
Azure Machine Learning
Show steps
-
Identify relevant tutorials on the Azure Machine Learning documentation site or Microsoft Learn.
-
Follow the step-by-step instructions to build and train machine learning models using Azure Machine Learning.
-
Troubleshoot any errors or issues encountered during the tutorials.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve practice problems on model evaluation metrics
Show steps
Solidify your understanding of different model evaluation metrics by solving practice problems that assess your ability to calculate and interpret them.
Browse courses on
Accuracy
Show steps
-
Gather practice problems from online resources or textbooks.
-
Calculate model evaluation metrics based on provided data or scenarios.
-
Analyze the results and identify patterns or insights.
Participate in peer-led discussions on model interpretability
Show steps
Expand your understanding of model interpretability by engaging in discussions with peers, exchanging ideas, and exploring different perspectives on this important aspect of ML.
Browse courses on
Model Interpretability
Show steps
-
Join or form a peer study group focused on model interpretability.
-
Prepare for discussions by reading relevant articles or research papers.
-
Actively participate in discussions, share insights, and ask clarifying questions.
Develop a case study showcasing an ML model improvement
Show steps
Apply your knowledge to a practical scenario by developing a case study that demonstrates how you improved the accuracy, efficiency, or explainability of an ML model.
Browse courses on
Case study
Show steps
-
Choose a specific ML model and identify areas for improvement.
-
Implement techniques to address the areas for improvement, such as data preprocessing, feature engineering, or algorithm optimization.
-
Evaluate the improved model and compare its performance to the original model.
-
Document your process and findings in a comprehensive case study.
Contribute to open-source projects related to Azure Machine Learning
Show steps
Deepen your understanding of Azure Machine Learning and contribute to the community by participating in open-source projects.
Browse courses on
Open Source
Show steps
-
Identify open-source projects related to Azure Machine Learning on GitHub or other platforms.
-
Review the project documentation and identify areas where you can contribute.
-
Submit pull requests with your contributions, ensuring they meet the project's coding standards.
Identify industry experts or experienced practitioners for mentorship
Show steps
Enhance your learning experience by seeking guidance and support from experienced professionals in the field of ML.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Research potential mentors through online platforms, industry events, or professional networks.
- Reach out to individuals who align with your career goals and interests.
- Articulate your mentorship needs and goals clearly.
Review of basic Azure features and concepts
Show steps
Sharpen your existing knowledge of Azure to better grasp the concepts and techniques taught in this course.
Browse courses on
Azure
Show steps
Show steps
Show steps
- Go over the Azure portal interface and fundamental concepts such as resource groups, storage accounts, and virtual networks.
- Review Azure documentation and tutorials to refresh your memory on essential Azure services and tools.
Complete hands-on tutorials on Azure Machine Learning
Show steps
Reinforce your understanding of Azure Machine Learning by following guided tutorials that provide practical, hands-on experience.
Browse courses on
Azure Machine Learning
Show steps
Show steps
Show steps
- Identify relevant tutorials on the Azure Machine Learning documentation site or Microsoft Learn.
- Follow the step-by-step instructions to build and train machine learning models using Azure Machine Learning.
- Troubleshoot any errors or issues encountered during the tutorials.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve practice problems on model evaluation metrics
Show steps
Solidify your understanding of different model evaluation metrics by solving practice problems that assess your ability to calculate and interpret them.
Browse courses on
Accuracy
Show steps
Show steps
Show steps
- Gather practice problems from online resources or textbooks.
- Calculate model evaluation metrics based on provided data or scenarios.
- Analyze the results and identify patterns or insights.
Participate in peer-led discussions on model interpretability
Show steps
Expand your understanding of model interpretability by engaging in discussions with peers, exchanging ideas, and exploring different perspectives on this important aspect of ML.
Browse courses on
Model Interpretability
Show steps
Show steps
Show steps
- Join or form a peer study group focused on model interpretability.
- Prepare for discussions by reading relevant articles or research papers.
- Actively participate in discussions, share insights, and ask clarifying questions.
Develop a case study showcasing an ML model improvement
Show steps
Apply your knowledge to a practical scenario by developing a case study that demonstrates how you improved the accuracy, efficiency, or explainability of an ML model.
Browse courses on
Case study
Show steps
Show steps
Show steps
- Choose a specific ML model and identify areas for improvement.
- Implement techniques to address the areas for improvement, such as data preprocessing, feature engineering, or algorithm optimization.
- Evaluate the improved model and compare its performance to the original model.
- Document your process and findings in a comprehensive case study.
Contribute to open-source projects related to Azure Machine Learning
Show steps
Deepen your understanding of Azure Machine Learning and contribute to the community by participating in open-source projects.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open-source projects related to Azure Machine Learning on GitHub or other platforms.
- Review the project documentation and identify areas where you can contribute.
- Submit pull requests with your contributions, ensuring they meet the project's coding standards.
Career center
Learners who complete Evaluating Model Effectiveness in Microsoft Azure will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use data to build and validate machine learning models. They develop and deploy data-driven solutions to improve business outcomes. This course helps build a foundation for success in Data Science by teaching you how to evaluate and improve the effectiveness of ML models. You will learn how to identify and eliminate overfitting, implement ensembling, and assess ML model interpretability. These are all essential skills for Data Scientists who want to build accurate and reliable ML models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy ML models. They work with data scientists to identify business problems that can be solved with ML. This course may be useful for Machine Learning Engineers who want to improve the effectiveness of their ML models. You will learn how to evaluate model effectiveness, improve model performance, and assess model interpretability.
Data Analyst
Data Analyst
Data Analysts use data to identify trends and patterns. They develop reports and visualizations to help businesses make informed decisions. This course may be useful for Data Analysts who want to learn how to evaluate and improve the effectiveness of ML models. You will learn how to identify and eliminate overfitting, implement ensembling, and assess ML model interpretability.
Business Analyst
Business Analyst
Business Analysts identify and solve business problems. They work with stakeholders to gather requirements and develop solutions. This course may be useful for Business Analysts who want to learn how to evaluate and improve the effectiveness of ML models. You will learn how to identify and eliminate overfitting, implement ensembling, and assess ML model interpretability.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with stakeholders to gather requirements and develop solutions. This course may be useful for Software Engineers who want to learn how to evaluate and improve the effectiveness of ML models. You will learn how to identify and eliminate overfitting, implement ensembling, and assess ML model interpretability.
Product Manager
Product Manager
Product Managers develop and manage products. They work with stakeholders to gather requirements and develop solutions. This course may be useful for Product Managers who want to learn how to evaluate and improve the effectiveness of ML models. You will learn how to identify and eliminate overfitting, implement ensembling, and assess ML model interpretability.
Project Manager
Project Manager
Project Managers plan and execute projects. They work with stakeholders to gather requirements and develop solutions. This course may be useful for Project Managers who want to learn how to evaluate and improve the effectiveness of ML models. You will learn how to identify and eliminate overfitting, implement ensembling, and assess ML model interpretability.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work with data scientists and other stakeholders to gather requirements and develop solutions. This course may be useful for Data Engineers who want to learn how to evaluate and improve the effectiveness of ML models. You will learn how to identify and eliminate overfitting, implement ensembling, and assess ML model interpretability.
Database Administrator
Database Administrator
Database Administrators maintain and optimize databases. They work with stakeholders to gather requirements and develop solutions. This course may be useful for Database Administrators who want to learn how to evaluate and improve the effectiveness of ML models. You will learn how to identify and eliminate overfitting, implement ensembling, and assess ML model interpretability.
Systems Administrator
Systems Administrator
Systems Administrators maintain and optimize computer systems. They work with stakeholders to gather requirements and develop solutions. This course may be useful for Systems Administrators who want to learn how to evaluate and improve the effectiveness of ML models. You will learn how to identify and eliminate overfitting, implement ensembling, and assess ML model interpretability.
Mobile Developer
Mobile Developer
Mobile Developers design and develop mobile applications. They work with stakeholders to gather requirements and develop solutions. This course may be useful for Mobile Developers who want to learn how to evaluate and improve the effectiveness of ML models. You will learn how to identify and eliminate overfitting, implement ensembling, and assess ML model interpretability.
Web Developer
Web Developer
Web Developers design and develop websites. They work with stakeholders to gather requirements and develop solutions. This course may be useful for Web Developers who want to learn how to evaluate and improve the effectiveness of ML models. You will learn how to identify and eliminate overfitting, implement ensembling, and assess ML model interpretability.
Network Administrator
Network Administrator
Network Administrators maintain and optimize computer networks. They work with stakeholders to gather requirements and develop solutions. This course may be useful for Network Administrators who want to learn how to evaluate and improve the effectiveness of ML models. You will learn how to identify and eliminate overfitting, implement ensembling, and assess ML model interpretability.
Game Developer
Game Developer
Game Developers design and develop video games. They work with stakeholders to gather requirements and develop solutions. This course may be useful for Game Developers who want to learn how to evaluate and improve the effectiveness of ML models. You will learn how to identify and eliminate overfitting, implement ensembling, and assess ML model interpretability.
Security Analyst
Security Analyst
Security Analysts investigate and respond to security incidents. They work with stakeholders to gather requirements and develop solutions. This course may be useful for Security Analysts who want to learn how to evaluate and improve the effectiveness of ML models. You will learn how to identify and eliminate overfitting, implement ensembling, and assess ML model interpretability.
For more career information including salaries, visit:
OpenCourser.com/course/9jp885/evaluating
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
Evaluating Model Effectiveness in Microsoft Azure.
Save
Provides a comprehensive overview of deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It offers a theoretical foundation for understanding deep learning algorithms and valuable reference for those interested in gaining a deeper understanding of the field.
Provides a comprehensive overview of statistical learning methods, including topics such as linear and logistic regression, tree-based methods, and support vector machines. It offers a theoretical foundation for machine learning algorithms and valuable reference for those interested in gaining a deeper understanding of the field.
Provides a probabilistic perspective on machine learning, offering a theoretical foundation for understanding machine learning algorithms. It covers topics such as Bayesian inference, graphical models, and probabilistic programming. This book valuable resource for those interested in gaining a deeper understanding of the theoretical foundations of machine learning.
Provides a comprehensive introduction to machine learning, covering topics such as supervised and unsupervised learning, model selection, and evaluation. It offers a balance of theory and practical examples, making it suitable for both beginners and those with some prior knowledge of machine learning.
Focuses on interpretable machine learning techniques, providing insights into how machine learning models make predictions. It covers topics such as model agnostic methods, tree-based methods, and feature importance. This book is valuable for those interested in understanding and explaining the behavior of machine learning models.
Provides a practical guide to using Scikit-Learn, Keras, and TensorFlow for machine learning.
Provides a practical introduction to machine learning algorithms and techniques, using Python as the programming language. It covers topics such as supervised and unsupervised learning, model evaluation, and feature engineering. This book valuable reference for those looking to gain a deeper understanding of machine learning concepts.
Provides a thorough introduction to machine learning using the R programming language. It covers topics such as data manipulation, model fitting, and evaluation. This book valuable reference for those looking to gain proficiency in machine learning with R.
Provides a gentle introduction to machine learning using Python. It covers topics such as data preprocessing, feature engineering, and model evaluation. This book great starting point for those new to machine learning or looking to gain proficiency in Python for machine learning tasks.
Provides a comprehensive guide to using Python for deep learning.
Provides a comprehensive guide to using Python for machine learning.
Provides a comprehensive guide to using R with Azure Machine Learning.
Provides a comprehensive overview of machine learning for beginners.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9jp885/evaluating
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Time
50 hours
Level
Intermediate
Via
Pluralsight
Instructor
Tim Warner
Language
English
Good to know
Delves into assessing ML model interpretability, which is imperative for understanding and explaining model predictions
Utilizes Azure Machine Learning Studio, a popular platform for training and deploying ML models
Provides techniques to enhance model performance, such as eliminating overfitting and implementing ensembling
Taught by Tim Warner, an expert in data science and machine learning
Appropriate for data science professionals seeking to improve their ML model effectiveness
Requires prior knowledge of Azure Machine Learning Service, which may limit accessibility for beginners
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