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Snehan Kekre
Welcome to this project-based course on Visual Machine Learning with Yellowbrick. In this course, we will explore how to evaluate the performance of a random forest classifier on the Poker Hand data set using visual diagnostic tools from Yellowbrick. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: feature analysis, feature importance, algorithm selection, model evaluation using regression, cross-validation, and hyperparameter tuning.
Read more
Welcome to this project-based course on Visual Machine Learning with Yellowbrick. In this course, we will explore how to evaluate the performance of a random forest classifier on the Poker Hand data set using visual diagnostic tools from Yellowbrick. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: feature analysis, feature importance, algorithm selection, model evaluation using regression, cross-validation, and hyperparameter tuning.
Read more
Welcome to this project-based course on Visual Machine Learning with Yellowbrick. In this course, we will explore how to evaluate the performance of a random forest classifier on the Poker Hand data set using visual diagnostic tools from Yellowbrick. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: feature analysis, feature importance, algorithm selection, model evaluation using regression, cross-validation, and hyperparameter tuning.
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/okecxq/visual
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What's inside
Syllabus
Project: Visual Machine Learning with Yellowbrick
Welcome to this project-based course on Visual Machine Learning with Yellowbrick. In this course, we will explore how to evaluate the performance of a random forest classifier on the Poker Hand data set using visual diagnostic tools from Yellowbrick. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: feature analysis, feature importance, algorithm selection, model evaluation using regression, cross-validation, and hyperparameter tuning.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for beginners eager to enter the Machine Learning field
Leverages a cloud desktop interface for seamless hands-on learning
Focuses on visual machine learning techniques, which are valuable in industry
Covers comprehensive machine learning workflow steps, from data exploration to model evaluation
Emphasizes the practical application of machine learning concepts
Save this course
Save Visual Machine Learning with Yellowbrick to your list so you can find it easily later:
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Reviews summary
Helpful visual machine learning course
Learners say that Visual Machine Learning with Yellowbrick is a helpful course that provides practice quizzes. They greatly appreciate the guided projects included in the course.
Helpful practice quizzes included.
"This is a better-planned guided project with practice quizzes which really helps."
Course provides guided projects.
"This is a better-planned guided project.."
"really enjoyed this project."
"So I would definitely like to recommend this course for those who wants to have a knowledge on visual machine learning."
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 Visual Machine Learning with Yellowbrick with these
activities:
Take the Yellobrick tutorial
Show steps
Deepen your understanding of Python, Jupyter, and Yellowbrick before starting the course.
Browse courses on
Yellowbrick
Show steps
-
Go to the Yellowbrick website.
-
Click on the "Tutorials" tab.
-
Choose a tutorial that interests you.
-
Follow the steps in the tutorial.
Review 'Machine Learning for Beginners' by Manning Publications
Show steps
Strengthen your foundational understanding of machine learning concepts.
View
Machine Learning for Hackers
on Amazon
Show steps
-
Read through the book and highlight key concepts.
-
Summarize the main ideas of each chapter in your own words.
-
Discuss the book's content with peers or an instructor to enhance your understanding.
Follow the official Yellowbrick documentation
Show steps
Stay up-to-date on the latest features and best practices for using Yellowbrick.
Browse courses on
Yellowbrick
Show steps
-
Go to the Yellowbrick documentation website.
-
Read the tutorials.
-
Follow the code examples.
-
Try out the exercises.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Organize and review course materials
Show steps
Improve your retention and understanding by organizing and reviewing course materials.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
Organize materials by topic or module.
-
Review materials regularly to reinforce your learning.
Explore Yellowbrick documentation
Show steps
Familiarize yourself with Yellowbrick's capabilities to enhance your learning.
Browse courses on
Yellowbrick
Show steps
-
Visit the Yellowbrick website and read through the documentation.
-
Follow the tutorials provided by Yellowbrick to gain hands-on experience.
-
Experiment with different Yellowbrick visualization tools using the provided examples.
Join a Yellowbrick study group
Show steps
Connect with other learners and collaborate on projects to enhance your understanding.
Browse courses on
Yellowbrick
Show steps
-
Find a study group that meets your needs.
-
Attend the study group meetings.
-
Participate in discussions.
-
Work on projects with other members.
Practice feature analysis
Show steps
Enhance your understanding of feature analysis by working through practice problems.
Browse courses on
Feature Analysis
Show steps
-
Find online resources or textbooks that provide practice problems on feature analysis.
-
Set aside dedicated study time to work through the practice problems.
-
Review your answers and identify areas where you need improvement.
Practice using Yellowbrick's visualizers
Show steps
Strengthen your skills in using Yellowbrick to analyze and visualize machine learning data.
Browse courses on
Machine Learning Tools
Show steps
-
Load the Yellowbrick documentation.
-
Choose a visualizer that interests you.
-
Follow the instructions for using the visualizer.
-
Experiment with different settings.
Form peer study groups
Show steps
Improve your understanding by discussing course concepts with peers.
Browse courses on
Python
Show steps
-
Identify other students in the course who are interested in forming a study group.
-
Decide on a meeting schedule and location.
-
Prepare discussion topics for each meeting.
Mentor a junior developer or student who is learning Yellowbrick
Show steps
Deepen your understanding of Yellowbrick and help others succeed.
Browse courses on
Yellowbrick
Show steps
-
Find someone who is interested in learning Yellowbrick.
-
Set up a regular meeting time.
-
Share your knowledge and experience.
-
Answer their questions.
Write a blog post or article about your experience using Yellowbrick
Show steps
Share your knowledge and insights about Yellowbrick with the broader community.
Browse courses on
Data Visualization
Show steps
-
Choose a topic that you're knowledgeable about.
-
Write a draft of your post or article.
-
Get feedback from others.
-
Publish your post or article.
Create a visual representation of the Random Forest algorithm
Show steps
Solidify your understanding of the Random Forest algorithm by creating a visual representation.
Browse courses on
Random Forest
Show steps
-
Research different ways to visually represent the Random Forest algorithm.
-
Choose a representation method and gather the necessary data.
-
Create your visual representation using tools such as diagrams, flowcharts, or mind maps.
-
Present your visual representation to your peers or instructor for feedback.
Contribute to the Yellowbrick open-source project
Show steps
Deepen your understanding of Yellowbrick by contributing to its development.
Browse courses on
Yellowbrick
Show steps
-
Explore the Yellowbrick GitHub repository and identify areas where you can contribute.
-
Fork the repository and create a new branch for your changes.
-
Implement your changes and write unit tests to ensure functionality.
-
Submit a pull request to the main Yellowbrick repository.
Participate in a machine learning hackathon
Show steps
Challenge yourself and apply your skills in a real-world machine learning scenario.
Browse courses on
Machine Learning
Show steps
-
Find a machine learning hackathon that aligns with your interests and skill level.
-
Form a team or work independently on a project that addresses a specific problem statement.
-
Develop a machine learning solution and prepare a presentation for the hackathon.
-
Present your project to a panel of judges and receive feedback on your work.
Take the Yellobrick tutorial
Show steps
Deepen your understanding of Python, Jupyter, and Yellowbrick before starting the course.
Browse courses on
Yellowbrick
Show steps
Show steps
Show steps
- Go to the Yellowbrick website.
- Click on the "Tutorials" tab.
- Choose a tutorial that interests you.
- Follow the steps in the tutorial.
Review 'Machine Learning for Beginners' by Manning Publications
Show steps
Strengthen your foundational understanding of machine learning concepts.
View
Machine Learning for Hackers
on Amazon
Show steps
Show steps
Show steps
- Read through the book and highlight key concepts.
- Summarize the main ideas of each chapter in your own words.
- Discuss the book's content with peers or an instructor to enhance your understanding.
Follow the official Yellowbrick documentation
Show steps
Stay up-to-date on the latest features and best practices for using Yellowbrick.
Browse courses on
Yellowbrick
Show steps
Show steps
Show steps
- Go to the Yellowbrick documentation website.
- Read the tutorials.
- Follow the code examples.
- Try out the exercises.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Organize and review course materials
Show steps
Improve your retention and understanding by organizing and reviewing course materials.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- Organize materials by topic or module.
- Review materials regularly to reinforce your learning.
Explore Yellowbrick documentation
Show steps
Familiarize yourself with Yellowbrick's capabilities to enhance your learning.
Browse courses on
Yellowbrick
Show steps
Show steps
Show steps
- Visit the Yellowbrick website and read through the documentation.
- Follow the tutorials provided by Yellowbrick to gain hands-on experience.
- Experiment with different Yellowbrick visualization tools using the provided examples.
Join a Yellowbrick study group
Show steps
Connect with other learners and collaborate on projects to enhance your understanding.
Browse courses on
Yellowbrick
Show steps
Show steps
Show steps
- Find a study group that meets your needs.
- Attend the study group meetings.
- Participate in discussions.
- Work on projects with other members.
Practice feature analysis
Show steps
Enhance your understanding of feature analysis by working through practice problems.
Browse courses on
Feature Analysis
Show steps
Show steps
Show steps
- Find online resources or textbooks that provide practice problems on feature analysis.
- Set aside dedicated study time to work through the practice problems.
- Review your answers and identify areas where you need improvement.
Practice using Yellowbrick's visualizers
Show steps
Strengthen your skills in using Yellowbrick to analyze and visualize machine learning data.
Browse courses on
Machine Learning Tools
Show steps
Show steps
Show steps
- Load the Yellowbrick documentation.
- Choose a visualizer that interests you.
- Follow the instructions for using the visualizer.
- Experiment with different settings.
Form peer study groups
Show steps
Improve your understanding by discussing course concepts with peers.
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify other students in the course who are interested in forming a study group.
- Decide on a meeting schedule and location.
- Prepare discussion topics for each meeting.
Mentor a junior developer or student who is learning Yellowbrick
Show steps
Deepen your understanding of Yellowbrick and help others succeed.
Browse courses on
Yellowbrick
Show steps
Show steps
Show steps
- Find someone who is interested in learning Yellowbrick.
- Set up a regular meeting time.
- Share your knowledge and experience.
- Answer their questions.
Write a blog post or article about your experience using Yellowbrick
Show steps
Share your knowledge and insights about Yellowbrick with the broader community.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a topic that you're knowledgeable about.
- Write a draft of your post or article.
- Get feedback from others.
- Publish your post or article.
Create a visual representation of the Random Forest algorithm
Show steps
Solidify your understanding of the Random Forest algorithm by creating a visual representation.
Browse courses on
Random Forest
Show steps
Show steps
Show steps
- Research different ways to visually represent the Random Forest algorithm.
- Choose a representation method and gather the necessary data.
- Create your visual representation using tools such as diagrams, flowcharts, or mind maps.
- Present your visual representation to your peers or instructor for feedback.
Contribute to the Yellowbrick open-source project
Show steps
Deepen your understanding of Yellowbrick by contributing to its development.
Browse courses on
Yellowbrick
Show steps
Show steps
Show steps
- Explore the Yellowbrick GitHub repository and identify areas where you can contribute.
- Fork the repository and create a new branch for your changes.
- Implement your changes and write unit tests to ensure functionality.
- Submit a pull request to the main Yellowbrick repository.
Participate in a machine learning hackathon
Show steps
Challenge yourself and apply your skills in a real-world machine learning scenario.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a machine learning hackathon that aligns with your interests and skill level.
- Form a team or work independently on a project that addresses a specific problem statement.
- Develop a machine learning solution and prepare a presentation for the hackathon.
- Present your project to a panel of judges and receive feedback on your work.
Career center
Learners who complete Visual Machine Learning with Yellowbrick will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. Visual Machine Learning with Yellowbrick is a great course for aspiring Data Scientists because it provides them with the skills to visually evaluate the performance of machine learning models. This course will help Data Scientists to make better decisions about which models to use and how to tune them for optimal performance.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for building and deploying machine learning models. Visual Machine Learning with Yellowbrick is a great course for aspiring Machine Learning Engineers because it provides them with the skills to visually evaluate the performance of machine learning models. This course will help Machine Learning Engineers to build more accurate and reliable models.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. Visual Machine Learning with Yellowbrick is a great course for aspiring Data Analysts because it provides them with the skills to visually evaluate the performance of machine learning models. This course will help Data Analysts to make better decisions about which models to use and how to tune them for optimal performance.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. Visual Machine Learning with Yellowbrick is a great course for aspiring Business Intelligence Analysts because it provides them with the skills to visually evaluate the performance of machine learning models. This course will help Business Intelligence Analysts to make better decisions about which models to use and how to tune them for optimal performance.
Statistician
Statistician
Statisticians are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. Visual Machine Learning with Yellowbrick is a great course for aspiring Statisticians because it provides them with the skills to visually evaluate the performance of machine learning models. This course will help Statisticians to make better decisions about which models to use and how to tune them for optimal performance.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. Visual Machine Learning with Yellowbrick may be useful for aspiring Software Engineers because it provides them with the skills to visually evaluate the performance of machine learning models. This course may help Software Engineers to build more accurate and reliable software applications.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of new products. Visual Machine Learning with Yellowbrick may be useful for aspiring Product Managers because it provides them with the skills to visually evaluate the performance of machine learning models. This course may help Product Managers to make better decisions about which products to develop and how to market them.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. Visual Machine Learning with Yellowbrick may be useful for aspiring Marketing Managers because it provides them with the skills to visually evaluate the performance of machine learning models. This course may help Marketing Managers to make better decisions about which marketing campaigns to run and how to target them.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and motivating sales teams. Visual Machine Learning with Yellowbrick may be useful for aspiring Sales Managers because it provides them with the skills to visually evaluate the performance of machine learning models. This course may help Sales Managers to make better decisions about which sales strategies to use and how to target them.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data and making recommendations. Visual Machine Learning with Yellowbrick may be useful for aspiring Financial Analysts because it provides them with the skills to visually evaluate the performance of machine learning models. This course may help Financial Analysts to make better decisions about which investments to make and how to manage risk.
Operations Manager
Operations Manager
Operations Managers are responsible for planning and executing operations. Visual Machine Learning with Yellowbrick may be useful for aspiring Operations Managers because it provides them with the skills to visually evaluate the performance of machine learning models. This course may help Operations Managers to make better decisions about which processes to implement and how to improve efficiency.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for managing the human resources of an organization. Visual Machine Learning with Yellowbrick may be useful for aspiring Human Resources Managers because it provides them with the skills to visually evaluate the performance of machine learning models. This course may help Human Resources Managers to make better decisions about which hiring and firing decisions to make and how to develop employees.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with their products and services. Visual Machine Learning with Yellowbrick may be useful for aspiring Customer Success Managers because it provides them with the skills to visually evaluate the performance of machine learning models. This course may help Customer Success Managers to make better decisions about which customer service strategies to use and how to target them.
Project Manager
Project Manager
Project Managers are responsible for planning and executing projects. Visual Machine Learning with Yellowbrick may be useful for aspiring Project Managers because it provides them with the skills to visually evaluate the performance of machine learning models. This course may help Project Managers to make better decisions about which projects to undertake and how to manage them.
Consultant
Consultant
Consultants are responsible for providing advice and guidance to clients. Visual Machine Learning with Yellowbrick may be useful for aspiring Consultants because it provides them with the skills to visually evaluate the performance of machine learning models. This course may help Consultants to make better decisions about which advice to give and how to present it.
For more career information including salaries, visit:
OpenCourser.com/course/okecxq/visual
Reading list
We've selected 14 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
Visual Machine Learning with Yellowbrick.
Provides a rigorous and comprehensive treatment of machine learning algorithms and techniques.
Provides a comprehensive treatment of statistical learning methods, including machine learning algorithms.
Save
Provides a comprehensive treatment of deep learning algorithms and techniques.
Provides a probabilistic approach to machine learning, covering topics such as Bayesian inference and graphical models.
Provides a comprehensive introduction to reinforcement learning algorithms and techniques.
Provides a practical guide to machine learning techniques and algorithms using popular libraries like scikit-learn, Keras, and TensorFlow.
Provides a comprehensive overview of machine learning techniques and algorithms using R.
Provides a theoretical and algorithmic perspective on machine learning.
Provides a practical guide to machine learning techniques and algorithms using Python.
Provides a practical guide to applying machine learning techniques to business problems.
Provides a gentle introduction to machine learning concepts and algorithms, focusing on Python implementation.
Provides a concise and accessible introduction to machine learning concepts and algorithms.
Provides a practical guide to machine learning techniques and algorithms for non-technical readers.
Provides a non-technical introduction to machine learning concepts and algorithms.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/okecxq/visual
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Suitable for beginners eager to enter the Machine Learning field
Leverages a cloud desktop interface for seamless hands-on learning
Focuses on visual machine learning techniques, which are valuable in industry
Covers comprehensive machine learning workflow steps, from data exploration to model evaluation
Emphasizes the practical application of machine learning concepts
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