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Snehan Kekre
Welcome to this project-based course on Regression Analysis with Yellowbrick. In this project, we will build a machine learning model to predict the compressive strength of high performance concrete (HPC). Although, we will use linear regression, the emphasis of this project will be on using visualization techniques to steer our machine learning workflow. Visualization plays a crucial role throughout the analytical process. It is indispensable for any effective analysis, model selection, and evaluation. This project will make use of a diagnostic platform called Yellowbrick. It allows data scientists and machine learning practitioners to visualize the entire model selection process to steer towards better, more explainable models.Yellowbrick hosts several datasets from the UCI Machine Learning Repository. We’ll be working with the concrete dataset that is well suited for regression tasks. The dataset contains 1030 instances and 8 real valued attributes with a continuous target.
Read more
Welcome to this project-based course on Regression Analysis with Yellowbrick. In this project, we will build a machine learning model to predict the compressive strength of high performance concrete (HPC). Although, we will use linear regression, the emphasis of this project will be on using visualization techniques to steer our machine learning workflow. Visualization plays a crucial role throughout the analytical process. It is indispensable for any effective analysis, model selection, and evaluation. This project will make use of a diagnostic platform called Yellowbrick. It allows data scientists and machine learning practitioners to visualize the entire model selection process to steer towards better, more explainable models.Yellowbrick hosts several datasets from the UCI Machine Learning Repository. We’ll be working with the concrete dataset that is well suited for regression tasks. The dataset contains 1030 instances and 8 real valued attributes with a continuous target.
Read more
Welcome to this project-based course on Regression Analysis with Yellowbrick. In this project, we will build a machine learning model to predict the compressive strength of high performance concrete (HPC). Although, we will use linear regression, the emphasis of this project will be on using visualization techniques to steer our machine learning workflow. Visualization plays a crucial role throughout the analytical process. It is indispensable for any effective analysis, model selection, and evaluation. This project will make use of a diagnostic platform called Yellowbrick. It allows data scientists and machine learning practitioners to visualize the entire model selection process to steer towards better, more explainable models.Yellowbrick hosts several datasets from the UCI Machine Learning Repository. We’ll be working with the concrete dataset that is well suited for regression tasks. The dataset contains 1030 instances and 8 real valued attributes with a continuous target.
We we will cover the following topics in our machine learning workflow: exploratory data analysis (EDA), feature and target analysis, regression modelling, cross-validation, model evaluation, and hyperparamter 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/i4z3qz/regression
What's inside
Syllabus
Project: Regression Analysis with Yellowbrick
Welcome to this project-based course on Regression Analysis with Yellowbrick. In this project, we will build a machine learning model to predict the compressive strength of high performance concrete (HPC). Although, we will use linear regression, the emphasis of this project will be on using visualization techniques to steer our machine learning workflow. Visualization plays a crucial role throughout the analytical process. It is indispensable for any effective analysis, model selection, and evaluation. This project will make use of a diagnostic platform called Yellowbrick. It allows data scientists and machine learning practitioners to visualize the entire model selection process to steer towards better, more explainable models.Yellowbrick hosts several datasets from the UCI Machine Learning Repository. We’ll be working with the concrete dataset that is well suited for regression tasks. The dataset contains 1030 instances and 8 real valued attributes with a continuous target.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Emphasizes visualization techniques to steer machine learning workflow
Suitable for learners interested in regression analysis
Provides hands-on practice with Yellowbrick diagnostic platform
Adequate access to cloud desktop for project execution
May require prerequisite knowledge in regression analysis and linear regression
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Reviews summary
Yellowbrick regression course
Learners say this Regression Analysis with Yellowbrick class is a great way to learn about the Yellowbrick library. The interactive in-browser coding environment is seen as one of its many strengths, as it allows learners to apply new concepts immediately. The instructor is also praised for their clear delivery of engaging content. The one complaint noted in some of the reviews is that the theoretical side of the subject could be expanded upon.
Instructor excels at delivery of clear and engaging content.
"Great Instructor.Good Platform for Learning..."
"Excellent code and theory balance. Not too long nor too short. I wish the author could provide a PDF with all the concepts and theories compilation around the most important ideas."
Interactive coding environment lets you immediately apply new skills.
"the instructor need to include more theory"
"Nice and a very good project to do in week-end. "
A common complaint is that theoretical content is insufficient.
"This course could have been way better! The tutor tells you what the code does but does not explain why we use of what the plots actually mean."
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 Regression Analysis with Yellowbrick with these
activities:
Review Foundational Statistics
Show steps
Reviewing Statistical Methods for the Social Sciences will help you solidify your foundational knowledge of statistics, which will be essential for your success in Regression Analysis with Yellowbrick.
View
Statistical Methods for the Social Sciences,...
on Amazon
Show steps
-
Read Chapters 1-3 of the book.
-
Complete the practice problems at the end of each chapter.
-
Take a practice quiz to test your understanding of the material.
Follow the Course Instructor's Blog
Show steps
This activity will keep you up-to-date on the latest developments in regression analysis and Yellowbrick, and provide additional insights into the course material.
Browse courses on
Regression Analysis
Show steps
-
Subscribe to the instructor's blog
-
Read the latest posts
Review Basic Statistics
Show steps
This activity will ensure that you have a strong foundation in basic statistics, which is essential for understanding regression analysis.
Browse courses on
Statistics
Show steps
-
Review your notes from a previous statistics course
-
Take a practice quiz or test
Six other activities
Expand to see all activities and additional details
Show all nine activities
Complete the Yellowbrick Tutorial
Show steps
This activity will allow you to learn how to visualize data using Yellowbrick, a valuable tool for regression analysis.
Browse courses on
Visualization
Show steps
-
Go through the Yellowbrick tutorial
-
Complete the practice exercises
Review Linear Regression in Applied Statistical Classification
Show steps
This book provides a solid foundation for the theory and algorithms behind linear regression, which will be essential for understanding the course material.
View
The Elements of Statistical Learning
on Amazon
Show steps
-
Read Chapters 2 and 3 of the book.
-
Work through the exercises at the end of each chapter.
-
Apply the concepts to a real-world dataset.
Join a Study Group for Course Support
Show steps
This activity will provide you with a supportive environment to discuss the course material, ask questions, and learn from your peers.
Browse courses on
Regression Analysis
Show steps
-
Find a study group or create your own
-
Meet regularly to discuss the material
Build a Linear Regression Model
Show steps
Building a linear regression model from scratch will give you a deeper understanding of the concepts behind regression analysis.
Browse courses on
Linear Regression
Show steps
-
Fit the model to the training data.
-
Use scikit-learn to load the concrete dataset.
-
Split the dataset into training and testing sets.
-
Create a linear regression model.
-
Evaluate the model on the test data.
Build a Regression Model for a Real-World Dataset
Show steps
This activity will allow you to apply the concepts learned in the course to a real-world problem, reinforcing your understanding and skills.
Browse courses on
Regression Analysis
Show steps
-
Identify a suitable dataset
-
Clean and prepare the data
-
Build and train a regression model
-
Evaluate the model's performance
-
Write a report summarizing your findings
Compile Regression Resources
Show steps
Compiling a list ofregression resources will provide you with a valuable reference for future学习.
Browse courses on
Regression Analysis
Show steps
-
Create a list of regression analysis resources.
-
Include resources such as articles, videos, and books.
-
Organize the resources by topic.
Review Foundational Statistics
Show steps
Reviewing Statistical Methods for the Social Sciences will help you solidify your foundational knowledge of statistics, which will be essential for your success in Regression Analysis with Yellowbrick.
View
Statistical Methods for the Social Sciences,...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3 of the book.
- Complete the practice problems at the end of each chapter.
- Take a practice quiz to test your understanding of the material.
Follow the Course Instructor's Blog
Show steps
This activity will keep you up-to-date on the latest developments in regression analysis and Yellowbrick, and provide additional insights into the course material.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Subscribe to the instructor's blog
- Read the latest posts
Review Basic Statistics
Show steps
This activity will ensure that you have a strong foundation in basic statistics, which is essential for understanding regression analysis.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review your notes from a previous statistics course
- Take a practice quiz or test
Six other activities
Expand to see all activities and additional details
Show all nine activities
Complete the Yellowbrick Tutorial
Show steps
This activity will allow you to learn how to visualize data using Yellowbrick, a valuable tool for regression analysis.
Browse courses on
Visualization
Show steps
Show steps
Show steps
- Go through the Yellowbrick tutorial
- Complete the practice exercises
Review Linear Regression in Applied Statistical Classification
Show steps
This book provides a solid foundation for the theory and algorithms behind linear regression, which will be essential for understanding the course material.
View
The Elements of Statistical Learning
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 2 and 3 of the book.
- Work through the exercises at the end of each chapter.
- Apply the concepts to a real-world dataset.
Join a Study Group for Course Support
Show steps
This activity will provide you with a supportive environment to discuss the course material, ask questions, and learn from your peers.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Find a study group or create your own
- Meet regularly to discuss the material
Build a Linear Regression Model
Show steps
Building a linear regression model from scratch will give you a deeper understanding of the concepts behind regression analysis.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Fit the model to the training data.
- Use scikit-learn to load the concrete dataset.
- Split the dataset into training and testing sets.
- Create a linear regression model.
- Evaluate the model on the test data.
Build a Regression Model for a Real-World Dataset
Show steps
This activity will allow you to apply the concepts learned in the course to a real-world problem, reinforcing your understanding and skills.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Identify a suitable dataset
- Clean and prepare the data
- Build and train a regression model
- Evaluate the model's performance
- Write a report summarizing your findings
Compile Regression Resources
Show steps
Compiling a list ofregression resources will provide you with a valuable reference for future学习.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Create a list of regression analysis resources.
- Include resources such as articles, videos, and books.
- Organize the resources by topic.
Career center
Learners who complete Regression Analysis with Yellowbrick will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use mathematical and statistical techniques to extract insights from data. They work with data from a variety of sources, including structured and unstructured data. Data Scientists typically have a strong background in mathematics, statistics, and computer science. The Regression Analysis with Yellowbrick course can help you develop the skills you need to be a successful Data Scientist. This course will help you learn how to use visualization techniques to steer your machine learning workflow. You will also learn how to build and evaluate machine learning models. These skills are essential for Data Scientists who want to be able to extract insights from data and make informed decisions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They work with a variety of machine learning algorithms and techniques. Machine Learning Engineers typically have a strong background in computer science and mathematics. The Regression Analysis with Yellowbrick course can help you develop the skills you need to be a successful Machine Learning Engineer. This course will help you learn how to use visualization techniques to steer your machine learning workflow. You will also learn how to build and evaluate machine learning models. These skills are essential for Machine Learning Engineers who want to be able to design, develop, and deploy machine learning models.
Data Analyst
Data Analyst
Data Analysts use data to make informed decisions. They work with data from a variety of sources, including structured and unstructured data. Data Analysts typically have a strong background in statistics and computer science. The Regression Analysis with Yellowbrick course can help you develop the skills you need to be a successful Data Analyst. This course will help you learn how to use visualization techniques to steer your machine learning workflow. You will also learn how to build and evaluate machine learning models. These skills are essential for Data Analysts who want to be able to make informed decisions.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. They work with a variety of data sources, including financial data, marketing data, and operational data. Business Analysts typically have a strong background in business and data analysis. The Regression Analysis with Yellowbrick course can help you develop the skills you need to be a successful Business Analyst. This course will help you learn how to use visualization techniques to steer your machine learning workflow. You will also learn how to build and evaluate machine learning models. These skills are essential for Business Analysts who want to be able to solve business problems using data.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, interpret, and present data. They work with data from a variety of sources, including surveys, experiments, and observational studies. Statisticians typically have a strong background in mathematics and statistics. The Regression Analysis with Yellowbrick course can help you develop the skills you need to be a successful Statistician. This course will help you learn how to use visualization techniques to steer your machine learning workflow. You will also learn how to build and evaluate machine learning models. These skills are essential for Statisticians who want to be able to collect, analyze, interpret, and present data.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment decisions. They work with data from a variety of sources, including financial statements, market data, and economic data. Financial Analysts typically have a strong background in finance and accounting. The Regression Analysis with Yellowbrick course can help you develop the skills you need to be a successful Financial Analyst. This course will help you learn how to use visualization techniques to steer your machine learning workflow. You will also learn how to build and evaluate machine learning models. These skills are essential for Financial Analysts who want to be able to make investment decisions.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to understand customer behavior and develop marketing campaigns. They work with data from a variety of sources, including surveys, website traffic data, and social media data. Marketing Analysts typically have a strong background in marketing and data analysis. The Regression Analysis with Yellowbrick course can help you develop the skills you need to be a successful Marketing Analyst. This course will help you learn how to use visualization techniques to steer your machine learning workflow. You will also learn how to build and evaluate machine learning models. These skills are essential for Marketing Analysts who want to be able to understand customer behavior and develop marketing campaigns.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve problems in business and industry. They work with data from a variety of sources, including financial data, production data, and logistics data. Operations Research Analysts typically have a strong background in mathematics, statistics, and computer science. The Regression Analysis with Yellowbrick course can help you develop the skills you need to be a successful Operations Research Analyst. This course will help you learn how to use visualization techniques to steer your machine learning workflow. You will also learn how to build and evaluate machine learning models. These skills are essential for Operations Research Analysts who want to be able to solve problems in business and industry.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and assess risks. They work with data from a variety of sources, including financial data, insurance data, and healthcare data. Risk Analysts typically have a strong background in mathematics, statistics, and finance. The Regression Analysis with Yellowbrick course can help you develop the skills you need to be a successful Risk Analyst. This course will help you learn how to use visualization techniques to steer your machine learning workflow. You will also learn how to build and evaluate machine learning models. These skills are essential for Risk Analysts who want to be able to identify and assess risks.
Software Engineer
Software Engineer
Software Engineers design, develop, and test software applications. They work with a variety of programming languages and technologies. Software Engineers typically have a strong background in computer science. The Regression Analysis with Yellowbrick course may be useful for Software Engineers who want to learn how to use machine learning techniques to improve the performance of their software applications.
Computer Scientist
Computer Scientist
Computer Scientists conduct research in the field of computer science. They work on a variety of topics, including artificial intelligence, machine learning, and computer graphics. Computer Scientists typically have a strong background in mathematics and computer science. The Regression Analysis with Yellowbrick course may be useful for Computer Scientists who want to learn how to use machine learning techniques to solve problems in their research.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. They work with data from a variety of sources, including economic data, financial data, and social data. Economists typically have a strong background in economics, mathematics, and statistics. The Regression Analysis with Yellowbrick course may be useful for Economists who want to learn how to use machine learning techniques to analyze economic data.
Financial Planner
Financial Planner
Financial Planners help individuals and families plan for their financial future. They work with a variety of financial products and services, including investments, insurance, and retirement planning. Financial Planners typically have a strong background in finance and economics. The Regression Analysis with Yellowbrick course may be useful for Financial Planners who want to learn how to use machine learning techniques to analyze financial data.
Insurance Actuary
Insurance Actuary
Insurance Actuaries use mathematical and statistical techniques to assess the risk of insurance claims. They work with data from a variety of sources, including insurance claims data, financial data, and demographic data. Insurance Actuaries typically have a strong background in mathematics, statistics, and finance. The Regression Analysis with Yellowbrick course may be useful for Insurance Actuaries who want to learn how to use machine learning techniques to analyze insurance data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. They work with data from a variety of sources, including financial statements, market data, and economic data. Quantitative Analysts typically have a strong background in finance, mathematics, and statistics. The Regression Analysis with Yellowbrick course may be useful for Quantitative Analysts who want to learn how to use machine learning techniques to analyze financial data.
For more career information including salaries, visit:
OpenCourser.com/course/i4z3qz/regression
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
Regression Analysis with Yellowbrick.
Provides a comprehensive treatment of regression analysis, covering a wide range of topics from simple linear regression to multiple regression. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive treatment of statistical learning methods, including linear regression, logistic regression, and tree-based methods. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive treatment of machine learning, covering a wide range of topics from supervised learning to unsupervised learning. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive introduction to statistical learning methods, including linear regression, logistic regression, and tree-based methods. It valuable resource for both beginners and experienced practitioners.
Save
Provides a comprehensive treatment of deep learning, covering a wide range of topics from neural networks to convolutional neural networks. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive treatment of reinforcement learning, covering a wide range of topics from Markov decision processes to deep reinforcement learning. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive treatment of Bayesian reasoning and machine learning, covering a wide range of topics from Bayesian inference to graphical models. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive treatment of causal inference in statistics, covering a wide range of topics from causal graphs to counterfactuals. It valuable resource for both beginners and experienced practitioners.
Save
Provides a comprehensive treatment of time series analysis, covering a wide range of topics from forecasting to control. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive treatment of econometric analysis of cross section and panel data, covering a wide range of topics from linear regression to nonlinear regression. It valuable resource for both beginners and experienced practitioners.
Save
Provides a practical guide to predictive modeling, covering a wide range of topics from data preparation to model evaluation. It valuable resource for both beginners and experienced practitioners.
Save
Provides a comprehensive treatment of data mining techniques, covering a wide range of topics from data preprocessing to model evaluation. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive treatment of artificial intelligence, covering a wide range of topics from search and planning to natural language processing. It valuable resource for both beginners and experienced practitioners.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/i4z3qz/regression
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Emphasizes visualization techniques to steer machine learning workflow
Suitable for learners interested in regression analysis
Provides hands-on practice with Yellowbrick diagnostic platform
Adequate access to cloud desktop for project execution
May require prerequisite knowledge in regression analysis and linear regression
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