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Chetan Prabhu
Review the techniques and metrics used to evaluate how well your machine learning model performs. You will also learn methods to select the best machine learning model from a set of models that you've built.
Read more
Review the techniques and metrics used to evaluate how well your machine learning model performs. You will also learn methods to select the best machine learning model from a set of models that you've built.
Read more
Review the techniques and metrics used to evaluate how well your machine learning model performs. You will also learn methods to select the best machine learning model from a set of models that you've built.
During the machine learning model building process, you will have to make some important decisions on how to evaluate how well your models perform, as well as how to select the best performing model. In this course, Model Evaluation and Selection Using scikit-learn, you will learn foundational knowledge/gain the ability to evaluate and select the best models. First, you will learn about a variety of metrics that you can use to evaluate how well your models are performing. Next, you will discover techniques for selecting the model that will perform the best in the future. Finally, you will explore how to implement this knowledge in Python, using the scikit-learn library. When you're finished with this course, you will have the skills and knowledge of needed to evaluate and select the best machine learning model from a set of models that you've built.
Register for this course and see more details by visiting:
OpenCourser.com/course/9bix93/model
What's inside
Syllabus
Course Overview
What Is Model Evaluation and Selection?
Evaluation Methods for Classification Models
Evaluation Methods for Regression Models
Read more
Syllabus
Course Overview
What Is Model Evaluation and Selection?
Evaluation Methods for Classification Models
Evaluation Methods for Regression Models
Read more
Model Selection Techniques
Putting It All Together
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops foundational knowledge needed to evaluate and select the best machine learning models
Uses Python and the scikit-learn library for practical implementation
Relevant for practitioners seeking to enhance their skills in evaluating and selecting machine learning models
Exploration of evaluation methods and techniques is valuable for building strong models
Provides a solid foundation for further exploration in machine learning model evaluation and selection
Save this course
Save Model Evaluation and Selection Using scikit-learn 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 Model Evaluation and Selection Using scikit-learn with these
activities:
Organize and Review Course Materials
Show steps
Enhance your understanding by systematically organizing and reviewing the materials provided in this course.
Show steps
-
Create a structured system for organizing notes, assignments, and other course materials.
-
Regularly review the organized materials to reinforce your learning.
Review Fundamentals of Machine Learning
Show steps
Strengthen your understanding of machine learning basics to complement what you'll learn in this course.
Browse courses on
Machine Learning Fundamentals
Show steps
-
Revisit key concepts like supervised and unsupervised learning, feature engineering, and model selection.
-
Solve practice problems to test your comprehension of these concepts.
Explore Scikit-learn Tutorials
Show steps
Familiarize yourself with the capabilities and functionalities of the Scikit-learn library.
Browse courses on
scikit-learn
Show steps
-
Follow online tutorials to learn how to use Scikit-learn for data preprocessing, model training, and evaluation.
-
Experiment with different Scikit-learn algorithms and parameters.
Two other activities
Expand to see all activities and additional details
Show all five activities
Practice Model Evaluation Techniques
Show steps
Increase your proficiency in evaluating the performance of machine learning models.
Browse courses on
Model Evaluation
Show steps
-
Work through practice exercises that cover different evaluation metrics, such as accuracy, precision, and recall.
-
Analyze the results of model evaluation to identify areas for improvement.
Develop a Machine Learning Model Selection Framework
Show steps
Apply your knowledge to create a framework that streamlines the process of evaluating and selecting machine learning models.
Browse courses on
Model Comparison
Show steps
-
Design a framework that includes steps for data preprocessing, model training, and evaluation.
-
Implement the framework in Python using Scikit-learn.
-
Test the framework on a real-world dataset.
Organize and Review Course Materials
Show steps
Enhance your understanding by systematically organizing and reviewing the materials provided in this course.
Show steps
Show steps
Show steps
- Create a structured system for organizing notes, assignments, and other course materials.
- Regularly review the organized materials to reinforce your learning.
Review Fundamentals of Machine Learning
Show steps
Strengthen your understanding of machine learning basics to complement what you'll learn in this course.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Revisit key concepts like supervised and unsupervised learning, feature engineering, and model selection.
- Solve practice problems to test your comprehension of these concepts.
Explore Scikit-learn Tutorials
Show steps
Familiarize yourself with the capabilities and functionalities of the Scikit-learn library.
Browse courses on
scikit-learn
Show steps
Show steps
Show steps
- Follow online tutorials to learn how to use Scikit-learn for data preprocessing, model training, and evaluation.
- Experiment with different Scikit-learn algorithms and parameters.
Two other activities
Expand to see all activities and additional details
Show all five activities
Practice Model Evaluation Techniques
Show steps
Increase your proficiency in evaluating the performance of machine learning models.
Browse courses on
Model Evaluation
Show steps
Show steps
Show steps
- Work through practice exercises that cover different evaluation metrics, such as accuracy, precision, and recall.
- Analyze the results of model evaluation to identify areas for improvement.
Develop a Machine Learning Model Selection Framework
Show steps
Apply your knowledge to create a framework that streamlines the process of evaluating and selecting machine learning models.
Browse courses on
Model Comparison
Show steps
Show steps
Show steps
- Design a framework that includes steps for data preprocessing, model training, and evaluation.
- Implement the framework in Python using Scikit-learn.
- Test the framework on a real-world dataset.
Career center
Learners who complete Model Evaluation and Selection Using scikit-learn will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is a software engineer who specializes in the development and maintenance of machine learning models. Machine Learning Engineers have a strong understanding of machine learning algorithms, data science, and software engineering. This course will help you understand the techniques used for evaluating and selecting the best performing machine learning model from a set of candidates.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software systems. Many modern software systems incorporate machine learning models as a core service. Understanding how to evaluate and select a machine learning model is becoming an essential skill for Software Engineers.
Statistician
Statistician
Statisticians use mathematical and statistical methods to collect, analyze, interpret, and present data. Machine learning is an important tool for statisticians to use, as it allows them to make predictions and inferences from data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. As a Quantitative Analyst, it is crucial to be able to evaluate and select the best performing machine learning model for a given problem.
Data Scientist
Data Scientist
A Data Scientist uses knowledge of modeling, machine learning, statistics, and probability to help companies make informed decisions by finding patterns and insights hidden in complex data. As a Data Scientist, you will play a key role in many industries, such as healthcare, finance, retail, and social media. If you are interested in this career, Model Evaluation and Selection Using scikit-learn can be a valuable building block in your understanding of machine learning.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex problems in business and industry. Machine learning is an important tool for Operations Research Analysts, as it allows them to develop more accurate and efficient models.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to help companies make informed decisions. As a Data Analyst, it is essential to be able to evaluate and select the machine learning model that will perform the best on the data. This course will provide you with the knowledge and skills you need to do this effectively.
Market Researcher
Market Researcher
Market Researchers use research methods to collect, analyze, and interpret data about markets and customers. Machine learning is an important tool for Market Researchers, as it allows them to quickly and easily extract insights from large datasets.
Business Analyst
Business Analyst
Business Analysts use data and analysis to help businesses make better decisions. Machine learning is an important tool for Business Analysts, as it allows them to automate and improve many of the tasks they perform.
Product Manager
Product Manager
Product Managers are responsible for the development and marketing of products. Machine learning is becoming increasingly important in product development, as it can be used to improve product quality and customer experience.
Human-Computer Interaction Designer
Human-Computer Interaction Designer
Human-Computer Interaction Designers design and develop user interfaces for software and websites. Machine learning is an important tool for Human-Computer Interaction Designers, as it can be used to improve the usability and effectiveness of user interfaces.
Technical Writer
Technical Writer
Technical Writers create and edit technical documentation, such as user manuals, training guides, and white papers. Machine learning is an important tool for Technical Writers, as it can be used to automate and improve the writing process.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for building relationships with customers and ensuring that they are satisfied with a company's products and services. Machine learning is an important tool for Customer Success Managers, as it can be used to identify potential problems and opportunities for improvement.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and motivating a team of salespeople. Machine learning is an important tool for Sales Managers, as it can be used to identify and target new customers.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for planning and executing marketing campaigns. Machine learning is an important tool for Marketing Managers, as it can be used to segment customers and target marketing campaigns more effectively.
For more career information including salaries, visit:
OpenCourser.com/course/9bix93/model
Reading list
We've selected 21 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
Model Evaluation and Selection Using scikit-learn.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow practical guide to machine learning using Python. provides a comprehensive overview of machine learning concepts and techniques, and it great resource for those who want to learn more about using scikit-learn for model evaluation and selection.
Save
Provides a comprehensive overview of machine learning concepts and techniques for practitioners. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and evaluation.
Python Machine Learning comprehensive guide to machine learning using Python. covers a wide range of topics, including model evaluation and selection, and it great resource for those who want to learn more about the practical aspects of machine learning.
Save
Machine Learning classic textbook on machine learning. provides a theoretical foundation for machine learning, and it great resource for those who want to learn more about the underlying principles of machine learning.
Save
Provides a comprehensive overview of machine learning concepts and techniques for practitioners. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and evaluation.
Provides a comprehensive overview of machine learning concepts and techniques. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and evaluation.
Bayesian Reasoning and Machine Learning textbook on machine learning that is written from a Bayesian perspective. provides a comprehensive overview of machine learning concepts and techniques, and it great resource for those who want to learn more about the theoretical foundations of machine learning.
Probabilistic Graphical Models textbook on probabilistic graphical models. provides a comprehensive overview of probabilistic graphical models and their applications, and it great resource for those who want to learn more about the theoretical foundations of machine learning.
Provides a comprehensive overview of machine learning concepts and techniques for practitioners. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and evaluation.
Save
Deep Learning textbook on deep learning. provides a comprehensive overview of deep learning concepts and techniques, and it great resource for those who want to learn more about the practical aspects of deep learning.
Provides a comprehensive overview of machine learning concepts and techniques. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and evaluation.
Machine Learning for Hackers practical guide to machine learning for non-technical readers. provides a gentle introduction to machine learning concepts and techniques, and it great resource for those who want to learn more about the practical aspects of machine learning.
Provides a comprehensive overview of statistical learning concepts and techniques. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and evaluation.
The Hundred-Page Machine Learning Book short and concise introduction to machine learning. provides a gentle introduction to machine learning concepts and techniques, and it great resource for those who want to learn more about the basics of machine learning.
Machine Learning with R textbook on machine learning using R. provides a comprehensive overview of machine learning concepts and techniques, and it great resource for those who want to learn more about the practical aspects of machine learning.
Machine Learning with Python Cookbook cookbook of machine learning recipes using Python. provides a collection of practical recipes for machine learning tasks, and it great resource for those who want to learn more about the practical aspects of machine learning.
Provides a comprehensive overview of machine learning concepts and techniques from a probabilistic perspective. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and evaluation.
Machine Learning Algorithms textbook on machine learning algorithms. provides a comprehensive overview of machine learning algorithms and their applications, and it great resource for those who want to learn more about the theoretical foundations of machine learning.
Save
Provides a comprehensive overview of machine learning concepts and techniques from an algorithmic perspective. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and evaluation.
Save
Provides a comprehensive overview of machine learning concepts and techniques from a Bayesian and optimization perspective. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and evaluation.
Provides a comprehensive overview of machine learning concepts and techniques from a theoretical perspective. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9bix93/model
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Time
77 hours
Level
Intermediate
Via
Pluralsight
Instructor
Chetan Prabhu
Language
English
Good to know
Develops foundational knowledge needed to evaluate and select the best machine learning models
Uses Python and the scikit-learn library for practical implementation
Relevant for practitioners seeking to enhance their skills in evaluating and selecting machine learning models
Exploration of evaluation methods and techniques is valuable for building strong models
Provides a solid foundation for further exploration in machine learning model evaluation and selection
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