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Snehan Kekre
Welcome to this project-based course on Performing Feature Analysis with Yellowbrick. In this course, we are going to use visualizations to steer machine learning workflows. 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. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: feature analysis using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization.
Read more
Welcome to this project-based course on Performing Feature Analysis with Yellowbrick. In this course, we are going to use visualizations to steer machine learning workflows. 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. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: feature analysis using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization.
Read more
Welcome to this project-based course on Performing Feature Analysis with Yellowbrick. In this course, we are going to use visualizations to steer machine learning workflows. 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. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: feature analysis using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization.
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/pcpo1m/perform
What's inside
Syllabus
Project: Feature Analysis with Yellowbrick
Welcome to this project-based course on Performing Feature Analysis with Yellowbrick. In this course, we are going to use visualizations to steer machine learning workflows. 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. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: feature analysis using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Emphasizes visual steering of the analysis process to help learners more clearly see how visualizations drive machine learning workflows
Course is project-based and hands-on, enabling learners to apply their knowledge through direct practice
Emphasizes feature analysis using various methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization
Uses the Yellowbrick library, which is a popular Python library for machine learning visualization
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Clear voice and helpful content
According to learners, this course on feature analysis with Yellowbrick is very helpful and has clear voice, but the voice sometimes isn't clear.
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 Perform Feature Analysis with Yellowbrick with these
activities:
Scatter plot analysis exercises
Show steps
Understanding scatter plots is crucial for visualizing relationships between different features.
Browse courses on
Scatter Plots
Show steps
-
Load the dataset
-
Create a scatter plot matrix
-
Identify patterns and relationships
RadViz visualization exercises
Show steps
RadViz is a powerful visualization technique for exploring high-dimensional data. Practicing with exercises will improve your understanding of it.
Show steps
-
Familiarize yourself with the RadViz algorithm
-
Visualize data using RadViz
-
Interpret the results
Parallel coordinates plot exercises
Show steps
Parallel coordinates plots are useful for visualizing high-dimensional data. Exercises will help you master this technique.
Show steps
-
Create a parallel coordinates plot
-
Identify patterns and relationships
-
Draw conclusions from the visualization
Five other activities
Expand to see all activities and additional details
Show all eight activities
Feature ranking exercises
Show steps
Feature ranking is essential for identifying the most important features in a dataset. Exercises will strengthen this skill.
Show steps
-
Implement feature ranking algorithms
-
Evaluate the performance of different ranking methods
-
Use feature ranking to select the most informative features
Manifold visualization exercises
Show steps
Manifold visualization helps visualize complex data relationships. Exercises will provide hands-on experience with this technique.
Show steps
-
Learn about dimensionality reduction techniques
-
Apply manifold visualization algorithms to real-world datasets
-
Interpret and draw insights from the visualizations
Create a summary of the key concepts in feature analysis
Show steps
Creating a summary will help you solidify your understanding of the concepts and their applications.
Browse courses on
Feature Analysis
Show steps
-
Identify the key concepts in feature analysis
-
Research and gather information on each concept
-
Organize and structure the information
-
Write a concise and informative summary
Develop a cheat sheet on using Yellowbrick for feature analysis
Show steps
A cheat sheet will provide you with quick reference to key Yellowbrick functionalities for feature analysis, aiding in your workflow.
Browse courses on
Yellowbrick
Show steps
-
Familiarize yourself with the Yellowbrick library
-
Identify the most commonly used Yellowbrick functions for feature analysis
-
Create a concise and well-organized cheat sheet
Attend a local Meetup or webinar on feature analysis
Show steps
Attending workshops or webinars led by experts can provide valuable insights and practical knowledge.
Browse courses on
Feature Analysis
Show steps
-
Search for local Meetups or webinars on feature analysis
-
Register and attend the event
-
Actively participate and engage with the speakers
Scatter plot analysis exercises
Show steps
Understanding scatter plots is crucial for visualizing relationships between different features.
Browse courses on
Scatter Plots
Show steps
Show steps
Show steps
- Load the dataset
- Create a scatter plot matrix
- Identify patterns and relationships
RadViz visualization exercises
Show steps
RadViz is a powerful visualization technique for exploring high-dimensional data. Practicing with exercises will improve your understanding of it.
Show steps
Show steps
Show steps
- Familiarize yourself with the RadViz algorithm
- Visualize data using RadViz
- Interpret the results
Parallel coordinates plot exercises
Show steps
Parallel coordinates plots are useful for visualizing high-dimensional data. Exercises will help you master this technique.
Show steps
Show steps
Show steps
- Create a parallel coordinates plot
- Identify patterns and relationships
- Draw conclusions from the visualization
Five other activities
Expand to see all activities and additional details
Show all eight activities
Feature ranking exercises
Show steps
Feature ranking is essential for identifying the most important features in a dataset. Exercises will strengthen this skill.
Show steps
Show steps
Show steps
- Implement feature ranking algorithms
- Evaluate the performance of different ranking methods
- Use feature ranking to select the most informative features
Manifold visualization exercises
Show steps
Manifold visualization helps visualize complex data relationships. Exercises will provide hands-on experience with this technique.
Show steps
Show steps
Show steps
- Learn about dimensionality reduction techniques
- Apply manifold visualization algorithms to real-world datasets
- Interpret and draw insights from the visualizations
Create a summary of the key concepts in feature analysis
Show steps
Creating a summary will help you solidify your understanding of the concepts and their applications.
Browse courses on
Feature Analysis
Show steps
Show steps
Show steps
- Identify the key concepts in feature analysis
- Research and gather information on each concept
- Organize and structure the information
- Write a concise and informative summary
Develop a cheat sheet on using Yellowbrick for feature analysis
Show steps
A cheat sheet will provide you with quick reference to key Yellowbrick functionalities for feature analysis, aiding in your workflow.
Browse courses on
Yellowbrick
Show steps
Show steps
Show steps
- Familiarize yourself with the Yellowbrick library
- Identify the most commonly used Yellowbrick functions for feature analysis
- Create a concise and well-organized cheat sheet
Attend a local Meetup or webinar on feature analysis
Show steps
Attending workshops or webinars led by experts can provide valuable insights and practical knowledge.
Browse courses on
Feature Analysis
Show steps
Show steps
Show steps
- Search for local Meetups or webinars on feature analysis
- Register and attend the event
- Actively participate and engage with the speakers
Career center
Learners who complete Perform Feature Analysis with Yellowbrick will develop knowledge and skills
that may be useful to these careers:
Machine Learning Manager
Machine Learning Manager
Machine Learning Managers lead and manage teams of Machine Learning Engineers. This course may be useful for Machine Learning Managers as it teaches techniques for visualizing and analyzing features using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization. These visualization methods are used by Machine Learning Managers to help identify patterns and insights within data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning systems. This course may be useful for Machine Learning Engineers as it teaches techniques for visualizing and analyzing features using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization. These visualization methods are used by Machine Learning Engineers to help identify patterns and insights within data.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, interpret, and present data. This course may be useful for Statisticians as it teaches techniques for visualizing and analyzing features using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization. These visualization methods are used by Statisticians to help identify patterns and insights within data.
Data Architect
Data Architect
Data Architects design and build data architectures. This course may be useful for Data Architects as it teaches techniques for visualizing and analyzing features using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization. These visualization methods are used by Data Architects to help identify patterns and insights within data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers as it teaches techniques for visualizing and analyzing features using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization. These visualization methods are used by Software Engineers to help identify patterns and insights within data.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. This course may be useful for Data Analysts as it teaches techniques for visualizing and analyzing features using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization. These visualization methods are used by Data Analysts to help identify patterns and insights within data.
Product Manager
Product Manager
Product Managers define, develop, and manage products. This course may be useful for Product Managers as it teaches techniques for visualizing and analyzing features using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization. These visualization methods are used by Product Managers to help identify patterns and insights within data.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and systems. This course may be useful for Data Engineers as it teaches techniques for visualizing and analyzing features using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization. These visualization methods are used by Data Engineers to help identify patterns and insights within data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical modeling to analyze and interpret data. This course may be useful for Quantitative Analysts as it teaches techniques for visualizing and analyzing features using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization. These visualization methods are used by Quantitative Analysts to help identify patterns and insights within data.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to provide insights that help businesses make better decisions. This course may be useful for Business Intelligence Analysts as it teaches techniques for visualizing and analyzing features using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization. These visualization methods are used by Business Intelligence Analysts to help identify patterns and insights within data.
Research Analyst
Research Analyst
Research Analysts conduct research and analysis to provide insights that help businesses make better decisions. This course may be useful for Research Analysts as it teaches techniques for visualizing and analyzing features using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization. These visualization methods are used by Research Analysts to help identify patterns and insights within data.
User Experience Researcher
User Experience Researcher
User Experience Researchers study how people interact with products and services. This course may be useful for User Experience Researchers as it teaches techniques for visualizing and analyzing features using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization. These visualization methods are used by User Experience Researchers to help identify patterns and insights within data.
Data Scientist Manager
Data Scientist Manager
Data Scientist Managers lead and manage teams of Data Scientists. This course may be useful for Data Scientist Managers as it teaches techniques for visualizing and analyzing features using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization. These visualization methods are used by Data Scientist Managers to help identify patterns and insights within data.
Data Visualization Engineer
Data Visualization Engineer
Data Visualization Engineers design and build data visualizations that help people understand data. This course may be useful for Data Visualization Engineers as it teaches techniques for visualizing and analyzing features using methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization. These visualization methods are used by Data Visualization Engineers to help identify patterns and insights within data.
Data Scientist
Data Scientist
Data Scientists use their mastery of statistics, machine learning and data visualization to translate raw data into actionable business intelligence. This course may be useful for Data Scientists as the visual steering of analysis it teaches overlaps with aspects of visual data analysis that Data Scientists conduct when compiling insights from data.
For more career information including salaries, visit:
OpenCourser.com/course/pcpo1m/perform
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
Perform Feature Analysis with Yellowbrick.
Provides a comprehensive overview of feature engineering techniques, including feature selection, transformation, and creation. It is particularly useful for understanding the theoretical underpinnings of feature engineering and for gaining practical guidance on how to apply these techniques in machine learning projects.
Provides a comprehensive overview of statistical learning concepts and algorithms. It is particularly useful for gaining a deep understanding of the field of statistical learning and for learning about the latest developments and trends.
Provides a comprehensive overview of statistical learning concepts and algorithms. It is particularly useful for gaining a deep understanding of the field of statistical learning and for learning about the latest developments and trends.
Provides a comprehensive overview of pattern recognition and machine learning concepts and algorithms. It is particularly useful for gaining a deep understanding of the field of pattern recognition and machine learning and for learning about the latest developments and trends.
Provides a comprehensive overview of machine learning concepts and algorithms from a probabilistic perspective. It is particularly useful for gaining a deep understanding of the field of machine learning and for learning about the latest developments and trends.
Save
Provides a comprehensive overview of deep learning concepts and algorithms. It is particularly useful for gaining a deep understanding of the field of deep learning and for learning about the latest developments and trends.
Provides a practical introduction to deep learning with Python. It is particularly useful for gaining hands-on experience with deep learning and for learning how to build and train deep learning models.
Provides a collection of recipes for performing various machine learning tasks in Python. It is particularly useful as a reference guide for specific machine learning tasks, and for gaining practical experience with Python libraries such as scikit-learn.
Provides a comprehensive introduction to Python for data analysis. It is particularly useful for gaining a strong foundation in Python programming and for learning how to use Python libraries such as pandas and numpy for data manipulation and analysis.
Provides a practical introduction to machine learning with Python libraries such as scikit-learn, Keras, and TensorFlow. It is particularly useful for gaining hands-on experience with these libraries and for learning how to build and train machine learning models.
Provides a comprehensive introduction to reinforcement learning concepts and algorithms. It is particularly useful for gaining a deep understanding of the field of reinforcement learning and for learning about the latest developments and trends.
Provides a practical introduction to machine learning concepts and algorithms. It is particularly useful for gaining hands-on experience with machine learning and for learning how to build and train machine learning models.
Save
Provides a practical introduction to data visualization techniques. It is particularly useful for gaining a foundational understanding of data visualization principles and for learning how to create effective visualizations using tools such as matplotlib and seaborn.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/pcpo1m/perform
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Emphasizes visual steering of the analysis process to help learners more clearly see how visualizations drive machine learning workflows
Course is project-based and hands-on, enabling learners to apply their knowledge through direct practice
Emphasizes feature analysis using various methods such as scatter plots, RadViz, parallel coordinates plots, feature ranking, and manifold visualization
Uses the Yellowbrick library, which is a popular Python library for machine learning visualization
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