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Evaluate Machine Learning Models with Yellowbrick
Snehan Kekre
Snehan Kekre
Welcome to this project-based course on Evaluating Machine Learning Models with Yellowbrick. In this course, we are going to use visualizations to steer our machine learning workflow. The problem we will tackle is to predict whether rooms in apartments are occupied or unoccupied based on passive sensor data such as temperature, humidity, light and CO2 levels. We will build a logistic regression model for binary classification. This is a continuation of the course on Room Occupancy Detection. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: model evaluation with ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models.
Read more
Welcome to this project-based course on Evaluating Machine Learning Models with Yellowbrick. In this course, we are going to use visualizations to steer our machine learning workflow. The problem we will tackle is to predict whether rooms in apartments are occupied or unoccupied based on passive sensor data such as temperature, humidity, light and CO2 levels. We will build a logistic regression model for binary classification. This is a continuation of the course on Room Occupancy Detection. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: model evaluation with ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models.
Read more
Welcome to this project-based course on Evaluating Machine Learning Models with Yellowbrick. In this course, we are going to use visualizations to steer our machine learning workflow. The problem we will tackle is to predict whether rooms in apartments are occupied or unoccupied based on passive sensor data such as temperature, humidity, light and CO2 levels. We will build a logistic regression model for binary classification. This is a continuation of the course on Room Occupancy Detection. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: model evaluation with ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models.
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, Yellowbrick, and scikit-learn pre-installed.
Notes:
- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/f7sqfh/evaluate
What's inside
Syllabus
Project: Evaluate Machine Learning Models with Yellowbrick
Welcome to this project-based course on Evaluating Machine Learning Models with Yellowbrick. In this course, we are going to use visualizations to steer our machine learning workflow. The problem we will tackle is to predict whether rooms in apartments are occupied or unoccupied based on passive sensor data such as temperature, humidity, light and CO2 levels. We will build a logistic regression model for binary classification. This is a continuation of the course on Room Occupancy Detection. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: model evaluation with ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches model evaluation techniques for improving machine learning workflow
Provides hands-on practice with Yellowbrick for model evaluation
Uses real-world sensor data to make the learning experience more applicable
Builds on the Room Occupancy Detection course, providing a continuation of the learning path
Requires learners to have a basic understanding of machine learning and Python programming
Assumes learners have access to cloud desktops and pre-configured software
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Reviews summary
Well-liked course on machine learning model evaluation
Learners say this course is highly effective at teaching how to evaluate machine learning models using Yellowbrick. Many students were very complimentary of the engaging assignments and the high quality of the teaching, but some felt that there could be more examples of how to apply models in practical situations. Overall, this course has received largely positive reviews and is recommended.
The hands-on exercises are a highlight of the course.
"It was great overall"
Many learners really enjoyed the lectures.
"Good"
"It's really good to learn"
The course could use more examples.
"But you need to fix the audio issues in some of the tasks."
"I really like this course but need a bit more information how to built the data and how to apply not only for visualizer. Especially, in data mining used knowledge feature based method. I wish get more information. and also while i do the coding with his course the most import can not defined gave me error name even i follow his steps, still same. Thanks"
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 Evaluate Machine Learning Models with Yellowbrick with these
activities:
Review binary classification
Show steps
This course will focus heavily on binary classification and will help you understand the concepts and techniques better.
Browse courses on
Binary Classification
Show steps
-
Review the concept of binary classification
-
Review different binary classification algorithms
Practice logistic regression problems
Show steps
The course will cover logistic regression and it will be helpful to practice applying this to solve relevant problems.
Show steps
-
Solve 5 logistic regression problems
Compile resources for machine learning evaluations
Show steps
Evaluating machine learning models will be a focus of the course so compiling relevant resources will be beneficial.
Show steps
-
Create a list of websites, articles, and tools related to machine learning evaluations
-
Write a summary report on the resources
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a cheat sheet on ROC/AUC plots
Show steps
ROC/AUC plots are an important topic in this course and creating a cheat sheet will help you understand and recall them better.
Show steps
-
List down the key concepts of ROC/AUC plots
-
Create a visual representation of ROC/AUC plots
Participate in peer study groups
Show steps
This course is project-based and working with your peers will facilitate working through projects and improving your learning.
Show steps
-
Find a study partner or group
-
Meet regularly to discuss course materials
Follow tutorials on confusion matrices
Show steps
Confusion matrices are a topic in the course and following tutorials will help you gain a deeper understanding.
Show steps
-
Find 3 tutorials on confusion matrices
-
Follow the tutorials and take notes
Participate in a machine learning competition
Show steps
This course focuses on evaluating machine learning models and participating in a competition will help you apply what you learn.
Show steps
-
Find an appropriate machine learning competition
-
Develop a model and submit it to the competition
Review binary classification
Show steps
This course will focus heavily on binary classification and will help you understand the concepts and techniques better.
Browse courses on
Binary Classification
Show steps
Show steps
Show steps
- Review the concept of binary classification
- Review different binary classification algorithms
Practice logistic regression problems
Show steps
The course will cover logistic regression and it will be helpful to practice applying this to solve relevant problems.
Show steps
Show steps
Show steps
- Solve 5 logistic regression problems
Compile resources for machine learning evaluations
Show steps
Evaluating machine learning models will be a focus of the course so compiling relevant resources will be beneficial.
Show steps
Show steps
Show steps
- Create a list of websites, articles, and tools related to machine learning evaluations
- Write a summary report on the resources
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a cheat sheet on ROC/AUC plots
Show steps
ROC/AUC plots are an important topic in this course and creating a cheat sheet will help you understand and recall them better.
Show steps
Show steps
Show steps
- List down the key concepts of ROC/AUC plots
- Create a visual representation of ROC/AUC plots
Participate in peer study groups
Show steps
This course is project-based and working with your peers will facilitate working through projects and improving your learning.
Show steps
Show steps
Show steps
- Find a study partner or group
- Meet regularly to discuss course materials
Follow tutorials on confusion matrices
Show steps
Confusion matrices are a topic in the course and following tutorials will help you gain a deeper understanding.
Show steps
Show steps
Show steps
- Find 3 tutorials on confusion matrices
- Follow the tutorials and take notes
Participate in a machine learning competition
Show steps
This course focuses on evaluating machine learning models and participating in a competition will help you apply what you learn.
Show steps
Show steps
Show steps
- Find an appropriate machine learning competition
- Develop a model and submit it to the competition
Career center
Learners who complete Evaluate Machine Learning Models with Yellowbrick will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing, deploying, and maintaining machine learning models. They use their knowledge of machine learning algorithms, data science, and software engineering to build models that can solve real-world problems. This course can help Machine Learning Engineers by providing them with a strong foundation in model evaluation techniques. The course covers topics such as ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This knowledge is essential for Machine Learning Engineers who want to build and deploy models that are accurate and reliable.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They use their knowledge of statistics, machine learning, and data mining to extract insights from data. This course can help Data Scientists by providing them with a strong foundation in model evaluation techniques. The course covers topics such as ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This knowledge is essential for Data Scientists who want to build and deploy models that are accurate and reliable.
Data Analyst
Data Analyst
Data Analysts use data to make informed decisions. They use their knowledge of statistics, data analysis, and data visualization to identify trends and patterns in data. This course can help Data Analysts by providing them with a strong foundation in model evaluation techniques. The course covers topics such as ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This knowledge is essential for Data Analysts who want to build and deploy models that are accurate and reliable.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use their knowledge of computer science, software engineering, and mathematics to build software that meets the needs of users. This course can help Software Engineers by providing them with a strong foundation in model evaluation techniques. The course covers topics such as ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This knowledge is essential for Software Engineers who want to build and deploy models that are accurate and reliable.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They use their knowledge of finance, mathematics, and statistics to make investment decisions. This course can help Quantitative Analysts by providing them with a strong foundation in model evaluation techniques. The course covers topics such as ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This knowledge is essential for Quantitative Analysts who want to build and deploy models that are accurate and reliable.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. They use their knowledge of insurance, mathematics, and statistics to make decisions about insurance products and premiums. This course can help Actuaries by providing them with a strong foundation in model evaluation techniques. The course covers topics such as ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This knowledge is essential for Actuaries who want to build and deploy models that are accurate and reliable.
Statistician
Statistician
Statisticians use mathematical and statistical models to analyze data. They use their knowledge of statistics, mathematics, and data analysis to make informed decisions. This course can help Statisticians by providing them with a strong foundation in model evaluation techniques. The course covers topics such as ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This knowledge is essential for Statisticians who want to build and deploy models that are accurate and reliable.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. They use their knowledge of data engineering, software engineering, and data science to build systems that collect, store, and process data. This course can help Data Engineers by providing them with a strong foundation in model evaluation techniques. The course covers topics such as ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This knowledge is essential for Data Engineers who want to build and deploy models that are accurate and reliable.
Business Analyst
Business Analyst
Business Analysts use data to make informed decisions about business operations. They use their knowledge of business, data analysis, and data visualization to identify trends and patterns in data. This course can help Business Analysts by providing them with a strong foundation in model evaluation techniques. The course covers topics such as ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This knowledge is essential for Business Analysts who want to build and deploy models that are accurate and reliable.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They use their knowledge of product management, marketing, and engineering to bring new products to market. This course can help Product Managers by providing them with a strong foundation in model evaluation techniques. The course covers topics such as ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This knowledge is essential for Product Managers who want to build and deploy models that are accurate and reliable.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment decisions. They use their knowledge of finance, accounting, and data analysis to evaluate companies and make investment recommendations. This course can help Financial Analysts by providing them with a strong foundation in model evaluation techniques. The course covers topics such as ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This knowledge is essential for Financial Analysts who want to build and deploy models that are accurate and reliable.
Risk Analyst
Risk Analyst
Risk Analysts use data to assess risk. They use their knowledge of risk management, data analysis, and statistics to evaluate risks and make decisions about how to mitigate them. This course can help Risk Analysts by providing them with a strong foundation in model evaluation techniques. The course covers topics such as ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This knowledge is essential for Risk Analysts who want to build and deploy models that are accurate and reliable.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior. They use their knowledge of marketing research, data analysis, and statistics to conduct research and make recommendations about marketing campaigns. This course can help Market Researchers by providing them with a strong foundation in model evaluation techniques. The course covers topics such as ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This knowledge is essential for Market Researchers who want to build and deploy models that are accurate and reliable.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. They use their knowledge of operations research, mathematics, and statistics to make decisions about how to improve business operations. This course can help Operations Research Analysts by providing them with a strong foundation in model evaluation techniques. The course covers topics such as ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This knowledge is essential for Operations Research Analysts who want to build and deploy models that are accurate and reliable.
Healthcare Analyst
Healthcare Analyst
Healthcare Analysts use data to improve healthcare outcomes. They use their knowledge of healthcare, data analysis, and statistics to conduct research and make recommendations about healthcare policies and practices. This course can help Healthcare Analysts by providing them with a strong foundation in model evaluation techniques. The course covers topics such as ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This knowledge is essential for Healthcare Analysts who want to build and deploy models that are accurate and reliable.
For more career information including salaries, visit:
OpenCourser.com/course/f7sqfh/evaluate
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
Evaluate Machine Learning Models with Yellowbrick.
Practical guide to building machine learning models using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of machine learning algorithms and techniques, making it a valuable resource for both beginners and experienced practitioners. This book can provide additional hands-on experience and practical examples.
Practical guide to building machine learning models using Python. It covers a wide range of machine learning algorithms and techniques, making it a valuable resource for beginners and experienced practitioners alike. This book can provide supplemental material and different perspectives on the concepts covered in the course.
Provides a comprehensive overview of statistical learning methods, including machine learning algorithms. It valuable resource for those who want to gain a deeper understanding of the statistical foundations of machine learning. While it is more theoretically oriented, it can provide valuable insights for those who want to gain a deeper understanding of the field.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers a wide range of topics, including Bayesian inference, graphical models, and reinforcement learning. While it is more advanced than other books on this list, it valuable resource for those who want to gain a deeper understanding of the probabilistic foundations of machine learning.
Save
Is the definitive textbook on deep learning. It provides a comprehensive overview of the field, covering the latest research and applications. While it is more advanced than other books on this list, it valuable resource for those who want to learn more about deep learning and its applications in various domains.
Provides a comprehensive overview of machine learning concepts and algorithms using R. It covers a wide range of topics, including data preprocessing, model building, and evaluation. While it is not specific to Python, it can provide valuable insights for those who want to learn more about machine learning using R.
Provides a hands-on introduction to data science using Python. It covers a wide range of topics, including data cleaning, data analysis, and machine learning. While it is not specific to machine learning model evaluation, it can provide valuable background knowledge for those who are new to data science and machine learning.
Provides a comprehensive overview of reinforcement learning, a type of machine learning that involves learning how to make decisions in an environment. While it is not directly related to the course topic, it can provide valuable insights for those who are interested in the broader field of machine learning.
Provides a comprehensive overview of natural language processing using Python. While it is not directly related to the course topic, it can provide valuable insights for those who are interested in applying machine learning to natural language processing tasks.
Save
Provides a comprehensive overview of the impact of big data on society. While it is not directly related to the course topic, it can provide valuable insights for those who are interested in the broader context of machine learning and its applications in the real world.
Save
Provides a comprehensive overview of the history and development of machine learning. While it is not directly related to the course topic, it can provide valuable insights for those who are interested in the broader context of machine learning and its potential impact on society.
Save
Explores the potential risks and benefits of artificial intelligence. While it is not directly related to the course topic, it can provide valuable insights for those who are interested in the broader societal implications of machine learning and AI.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/f7sqfh/evaluate
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Teaches model evaluation techniques for improving machine learning workflow
Provides hands-on practice with Yellowbrick for model evaluation
Uses real-world sensor data to make the learning experience more applicable
Builds on the Room Occupancy Detection course, providing a continuation of the learning path
Requires learners to have a basic understanding of machine learning and Python programming
Assumes learners have access to cloud desktops and pre-configured software
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