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Mark J Grover,
Miguel Maldonado,
and
Svitlana (Lana) Kramar
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
Read more
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
Read more
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
By the end of this course you should be able to:
Differentiate uses and applications of classification and regression in the context of supervised machine learning
Describe and use linear regression models
Use a variety of error metrics to compare and select a linear regression model that best suits your data
Articulate why regularization may help prevent overfitting
Use regularization regressions: Ridge, LASSO, and Elastic net
Who should take this course?
This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting.
What skills should you have?
To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.
Register for this course and see more details by visiting:
OpenCourser.com/course/5c3qi1/supervised
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What's inside
Syllabus
Introduction to Supervised Machine Learning and Linear Regression
This module introduces a brief overview of supervised machine learning and its main applications: classification and regression. After introducing the concept of regression, you will learn its best practices, as well as how to measure error and select the regression model that best suits your data.
Read more
Syllabus
Introduction to Supervised Machine Learning and Linear Regression
This module introduces a brief overview of supervised machine learning and its main applications: classification and regression. After introducing the concept of regression, you will learn its best practices, as well as how to measure error and select the regression model that best suits your data.
Read more
Data Splits and Polynomial Regression
There are a few best practices to avoid overfitting of your regression models. One of these best practices is splitting your data into training and test sets. Another alternative is to use cross validation. And a third alternative is to introduce polynomial features. This module walks you through the theoretical framework and a few hands-on examples of these best practices.
Cross Validation
There is a trade-off between the size of your training set and your testing set. If you use most of your data for training, you will have fewer samples to validate your model. Conversely, if you use more samples for testing, you will have fewer samples to train your model. Cross Validation will allow you to reuse your data to use more samples for training and testing.
Bias Variance Trade off and Regularization Techniques: Ridge, LASSO, and Elastic Net
This module walks you through the theory and a few hands-on examples of regularization regressions including ridge, LASSO, and elastic net. You will realize the main pros and cons of these techniques, as well as their differences and similarities.
Regularization Details
In this section, you will understand the relationship between the loss function and the different regularization types.
Final Project
In this section you will test everything you learned
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Meant for learners who want to transition into a role as a Data Scientist
Having a background in Python development and in Data Cleaning, EDA Calculus, Linear Algebra, Probability, and Statistics is recommended
Save this course
Save Supervised Machine Learning: Regression to your list so you can find it easily later:
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Reviews summary
Well-received ml course: regression
learners say this course is largely positive and engaging, with a strong emphasis on coding and practical applications. They appreciate the thorough explanations and well-structured content, which makes learning regression models and related concepts easy to follow and understand. This course is highly recommended for those looking to excel in machine learning, especially regression.
Instructor's explanations are clear and engaging
" The instructor from videos is amazing. Great tutor."
"Learned really about supervised learning and more importantly regularization and some available methods."
"Very well presented. This is without doubt the best series for Machine Learning on Coursera."
Clear and organized course structure
"Great course and very well structured."
"Very well designed course, great that we could work with our own data and apply the theory. Looking forward to continue the journey."
"The presentation of the different methods is very clear and well separated to understand the differences."
Detailed explanations and practical examples
"Great course, well structured. The presentation of the different methods is very clear and well separated to understand the differences."
"Thanks very much for this great course. It is comprehensive and intuitive in terms of Regression analysis. It covers all the necessary tools for an essential and sufficient application of Regression analysis."
"The course covered various aspects of regression modelling in good detail and the practice notebooks were also very helpful in implementing and reinforcing the learnings of course."
Emphasis on practical coding skills
"Great course for someone wanting to learn Supervise Machine learning"
"Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code."
"this course focuses mainly on codes and how to get your models trained well with the best results."
Instructor's speech lacks enthusiasm
"The instructor clearly reads a pre-written text and making his speech monotonic and hard to follow."
Not enough real-world examples provided
"Could be a bit more attention to common pittfalls and type and size of problems which are usually addressed by these methods."
Errors, typos, outdated content
"The course is incomplete on regression analysis. Also, the grading scale was biased after putting in a lot of time and effort(20 pages). The reason was I didn't follow the assignment questions."
"There are some syntax errors in the notebooks."
"Really difficult. Exams are not fully fair."
Lacking in-depth mathematical explanations
"The course lacks mathematical foundation. In order to fully understand the topic you need to read theory from other resources in parallel."
"This is a perfect course for learning and implementing supervised machine learning for regression tasks in Python. However, it does not have a comprehensive explanation of how linear regression and regularization work behind the scenes."
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 Supervised Machine Learning: Regression with these
activities:
Read "Elements of Statistical Learning"
Show steps
Reading "Elements of Statistical Learning" will provide you with a comprehensive understanding of the theoretical foundations and applications of regression analysis.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
-
Obtain a copy of "Elements of Statistical Learning" (either physical or digital).
-
Set aside dedicated time each week to read and study the material.
-
Take notes, highlight key concepts, and work through the exercises provided in the book.
Review Linear Algebra concepts
Show steps
Reviewing Linear Algebra concepts will help solidify your understanding of the mathematical foundations used in regression models.
Browse courses on
Linear Algebra
Show steps
-
Review your lecture notes and textbooks on Linear Algebra.
-
Solve practice problems related to matrix operations, vector spaces, and eigenvalues and eigenvectors.
-
Use online resources or consult with a tutor for additional support if needed.
Attend a workshop on machine learning with a focus on regression
Show steps
Participating in a workshop will provide you with an immersive learning experience and the opportunity to engage with experts in the field of machine learning and regression analysis.
Browse courses on
Machine Learning
Show steps
-
Research and find upcoming workshops on machine learning with a focus on regression.
-
Register for a workshop that aligns with your interests and schedule.
-
Attend the workshop, actively participate in discussions, and ask questions to clarify your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in a study group focused on regression analysis
Show steps
Engaging in a study group will provide you with a supportive environment to discuss concepts, share knowledge, and collaborate on regression analysis problems.
Browse courses on
Regression Analysis
Show steps
-
Find or create a study group with other students enrolled in the course.
-
Meet regularly to review course material, work on assignments together, and discuss regression analysis concepts.
-
Contribute actively to the group by sharing your understanding and helping others.
Complete coding exercises on regression models
Show steps
Practicing coding exercises will enhance your proficiency in implementing regression models using Python.
Browse courses on
Python
Show steps
-
Find online coding exercises or tutorials on regression models in Python.
-
Set aside dedicated time to work on these exercises, debugging your code as needed.
-
Challenge yourself by attempting more complex exercises as you progress.
Explore online tutorials on regularization techniques
Show steps
Following online tutorials will supplement your understanding of regularization techniques used in regression models.
Browse courses on
Regularization
Show steps
-
Search for reputable online tutorials or courses on regularization techniques.
-
Go through the tutorials, taking notes and practicing the concepts demonstrated.
-
Apply what you learn to your own regression models to see the impact of regularization.
Create a repository of regression model examples
Show steps
Creating a repository of regression model examples will help you build a practical understanding of how these models are applied in real-world scenarios.
Browse courses on
Regression Analysis
Show steps
-
Gather code examples and case studies of regression models from various sources (e.g., GitHub, Kaggle).
-
Organize these examples into a structured repository, categorizing them based on the type of regression model or application.
-
Review and analyze the examples to identify best practices and common pitfalls.
Develop a regression model for a real-world problem
Show steps
Working on a real-world project will provide you with hands-on experience in applying regression models to solve practical problems.
Browse courses on
Regression Analysis
Show steps
-
Identify a real-world problem that can be addressed using regression analysis.
-
Collect and prepare the necessary data for your model.
-
Train and evaluate several regression models to find the best fit for your data.
-
Deploy your model and monitor its performance over time.
Read "Elements of Statistical Learning"
Show steps
Reading "Elements of Statistical Learning" will provide you with a comprehensive understanding of the theoretical foundations and applications of regression analysis.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of "Elements of Statistical Learning" (either physical or digital).
- Set aside dedicated time each week to read and study the material.
- Take notes, highlight key concepts, and work through the exercises provided in the book.
Review Linear Algebra concepts
Show steps
Reviewing Linear Algebra concepts will help solidify your understanding of the mathematical foundations used in regression models.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review your lecture notes and textbooks on Linear Algebra.
- Solve practice problems related to matrix operations, vector spaces, and eigenvalues and eigenvectors.
- Use online resources or consult with a tutor for additional support if needed.
Attend a workshop on machine learning with a focus on regression
Show steps
Participating in a workshop will provide you with an immersive learning experience and the opportunity to engage with experts in the field of machine learning and regression analysis.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Research and find upcoming workshops on machine learning with a focus on regression.
- Register for a workshop that aligns with your interests and schedule.
- Attend the workshop, actively participate in discussions, and ask questions to clarify your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in a study group focused on regression analysis
Show steps
Engaging in a study group will provide you with a supportive environment to discuss concepts, share knowledge, and collaborate on regression analysis problems.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Find or create a study group with other students enrolled in the course.
- Meet regularly to review course material, work on assignments together, and discuss regression analysis concepts.
- Contribute actively to the group by sharing your understanding and helping others.
Complete coding exercises on regression models
Show steps
Practicing coding exercises will enhance your proficiency in implementing regression models using Python.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find online coding exercises or tutorials on regression models in Python.
- Set aside dedicated time to work on these exercises, debugging your code as needed.
- Challenge yourself by attempting more complex exercises as you progress.
Explore online tutorials on regularization techniques
Show steps
Following online tutorials will supplement your understanding of regularization techniques used in regression models.
Browse courses on
Regularization
Show steps
Show steps
Show steps
- Search for reputable online tutorials or courses on regularization techniques.
- Go through the tutorials, taking notes and practicing the concepts demonstrated.
- Apply what you learn to your own regression models to see the impact of regularization.
Create a repository of regression model examples
Show steps
Creating a repository of regression model examples will help you build a practical understanding of how these models are applied in real-world scenarios.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Gather code examples and case studies of regression models from various sources (e.g., GitHub, Kaggle).
- Organize these examples into a structured repository, categorizing them based on the type of regression model or application.
- Review and analyze the examples to identify best practices and common pitfalls.
Develop a regression model for a real-world problem
Show steps
Working on a real-world project will provide you with hands-on experience in applying regression models to solve practical problems.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed using regression analysis.
- Collect and prepare the necessary data for your model.
- Train and evaluate several regression models to find the best fit for your data.
- Deploy your model and monitor its performance over time.
Career center
Learners who complete Supervised Machine Learning: Regression will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
**Statisticians** collect, analyze, and interpret data to provide insights and make informed decisions. This course offers a structured approach to regression modeling, a fundamental statistical technique. By mastering the concepts and methods covered in this course, you will expand your statistical toolkit and strengthen your ability to conduct data analysis and draw meaningful conclusions.
Quantitative Analyst
Quantitative Analyst
**Quantitative Analysts** develop and implement mathematical and statistical models to solve complex financial problems. This course provides a hands-on approach to regression modeling, a fundamental technique in quantitative finance. By completing this course, you will acquire the skills to build and interpret regression models, enabling you to make informed investment decisions and contribute to the success of your organization.
Machine Learning Engineer
Machine Learning Engineer
**Machine Learning Engineers** design, develop, and deploy machine learning models to solve complex business problems. This course provides a hands-on introduction to regression modeling, which is a widely used technique in machine learning. By completing this course, you will gain a solid understanding of the principles and practices of regression modeling, empowering you to effectively apply it in your role as a Machine Learning Engineer.
Actuary
Actuary
**Actuaries** use mathematical and statistical techniques to assess and manage financial risks. This course offers a comprehensive introduction to regression modeling, a valuable tool for risk assessment and prediction. By completing this course, you will expand your actuarial toolkit and enhance your ability to analyze data, make informed decisions, and provide financial guidance.
Data Scientist
Data Scientist
**Data Scientists** analyze and interpret data to extract meaningful insights and make data-driven recommendations. This course provides a solid foundation in regression modeling, which is a fundamental skill for Data Scientists who work with continuous outcome variables. By mastering the concepts and techniques taught in this course, you will enhance your ability to build and evaluate regression models, enabling you to make accurate predictions and drive informed decision-making.
Market Researcher
Market Researcher
**Market Researchers** collect and analyze data to understand market trends and customer behavior. This course offers a comprehensive overview of regression modeling, a powerful tool for uncovering relationships and making predictions. By mastering the techniques taught in this course, you will enhance your ability to conduct market research and provide valuable insights to businesses.
Operations Research Analyst
Operations Research Analyst
**Operations Research Analysts** use mathematical and analytical techniques to solve complex business problems. This course offers a structured approach to regression modeling, a fundamental statistical technique used in operations research. By completing this course, you will expand your toolkit and enhance your ability to analyze data, optimize processes, and make informed decisions to improve operational efficiency.
Data Analyst
Data Analyst
**Data Analysts** collect, clean, and analyze data to provide insights and support decision-making. This course offers a comprehensive overview of regression modeling, a statistical technique used to analyze relationships between variables. By understanding the concepts and applications of regression modeling, you will be well-equipped to handle data analysis tasks and contribute to data-driven decision-making within your organization.
Business Analyst
Business Analyst
**Business Analysts** bridge the gap between business stakeholders and technical teams by analyzing business needs and translating them into technical requirements. This course provides a foundation in regression modeling, a valuable tool for understanding and predicting business outcomes. By gaining proficiency in regression techniques, you will enhance your ability to make data-informed recommendations and drive business value.
Risk Analyst
Risk Analyst
**Risk Analysts** assess and manage risks within organizations. This course provides a foundation in regression modeling, a statistical technique used to predict future outcomes. By understanding the principles and applications of regression modeling, you will be better equipped to quantify and mitigate risks, ensuring the financial stability and operational efficiency of your organization.
Financial Analyst
Financial Analyst
**Financial Analysts** evaluate financial data and make recommendations to businesses and investors. This course provides a practical introduction to regression modeling, a widely used technique in financial analysis. By completing this course, you will acquire the skills to build and interpret regression models, enabling you to make informed financial decisions and provide valuable insights to stakeholders.
Data Engineer
Data Engineer
**Data Engineers** design, build, and maintain data pipelines and infrastructure. While not a direct application of regression modeling, this course provides a foundation in statistical techniques that can be applied to data engineering tasks. By understanding the principles of regression modeling, you can enhance your ability to analyze data quality, identify data patterns, and develop efficient data processing pipelines.
Software Engineer
Software Engineer
**Software Engineers** design, develop, and maintain software systems. While not directly related to regression modeling, this course provides a foundation in statistical techniques that can be applied to software development tasks. By understanding the concepts of regression modeling, you can enhance your ability to analyze data, identify patterns, and make informed decisions in your software engineering role.
Computer Scientist
Computer Scientist
**Computer Scientists** research and develop new computing technologies and applications. This course offers a structured approach to regression modeling, a fundamental statistical technique. While not directly related to computer science, regression modeling can provide insights into complex systems and contribute to the development of innovative technologies.
Database Administrator
Database Administrator
**Database Administrators** manage and maintain database systems. While not directly related to regression modeling, this course provides a foundation in statistical techniques that can be applied to database management tasks. By understanding the concepts of regression modeling, you can enhance your ability to analyze database performance, identify data patterns, and optimize database queries.
For more career information including salaries, visit:
OpenCourser.com/course/5c3qi1/supervised
Reading list
We've selected 11 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
Supervised Machine Learning: Regression.
Classic reference on statistical learning methods. It provides a comprehensive overview of the theory and practice of regression analysis, as well as other supervised learning methods.
Provides a comprehensive overview of statistical learning methods, including regression models. It valuable resource for anyone who wants to learn more about the theory and practice of regression analysis.
Provides a comprehensive overview of pattern recognition and machine learning methods, including regression models. It valuable resource for anyone who wants to learn more about the theory and practice of machine learning.
Save
Provides a comprehensive overview of forecasting methods, including regression models. It valuable resource for anyone who wants to learn more about the theory and practice of forecasting.
Provides a comprehensive overview of time series analysis methods, including regression models. It valuable resource for anyone who wants to learn more about the theory and practice of time series analysis.
Provides a comprehensive overview of statistical methods used in the atmospheric sciences, including regression models. It valuable resource for anyone who wants to learn more about the theory and practice of regression analysis in an atmospheric science context.
Focuses on the application of regression models in actuarial and financial settings. It provides a practical guide to building and using regression models for a variety of purposes.
Focuses on the application of regression models to categorical variables. It provides a practical guide to building and using regression models for a variety of purposes.
Save
Provides a comprehensive overview of econometrics, which is the application of statistical methods to economic data. It provides a valuable resource for anyone who wants to learn more about the theory and practice of regression analysis in an economic context.
Provides a practical guide to regression analysis, with a focus on examples. It valuable resource for anyone who wants to learn more about the theory and practice of regression analysis.
Provides an overview of causal inference methods, which are essential for understanding the relationship between variables in regression models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5c3qi1/supervised
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Level
Intermediate
Via
Coursera
Institution
IBM
Instructors
Mark J Grover
Miguel Maldonado
Svitlana (Lana) Kramar
Language
English
Transcripts
Español
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آذربایجان دیلی
This course belongs to a
collection
called
IBM Introduction to Machine Learning. It's comprised of four courses:
- Supervised Machine Learning: Regression (viewing)
- Exploratory Data Analysis for Machine Learning
- Supervised Machine Learning: Classification
- Unsupervised Machine Learning
Good to know
Meant for learners who want to transition into a role as a Data Scientist
Having a background in Python development and in Data Cleaning, EDA Calculus, Linear Algebra, Probability, and Statistics is recommended
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