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PARTHA MAJUMDAR
This project is aimed at students and practitioners of Data Sciences for building Predictive Analytics models for research and commercial purposes.
Machine Learning can be used to solve prediction problems for classification and regression. In this project,...
Read more
This project is aimed at students and practitioners of Data Sciences for building Predictive Analytics models for research and commercial purposes.
Machine Learning can be used to solve prediction problems for classification and regression. In this project,...
Read more
This project is aimed at students and practitioners of Data Sciences for building Predictive Analytics models for research and commercial purposes.
Machine Learning can be used to solve prediction problems for classification and regression. In this project, we discuss about using Machine Learning for building Regression Models. We will use Python Language.
In Python, we have many options for building Machine Learning solutions like Tensor Flow, Keras, etc. In this project, we use Scikit-Learn. Scikit-Learn provides a comprehensive array of tools for building regression models. The concepts learnt in this project can be extended to build Neural Networks using Tensor Flow or Keras or any other tool.
This Guided Project was created by a Coursera community member.
Register for this course and see more details by visiting:
OpenCourser.com/course/cb4s39/regression
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Teaches methods for developing predictive analytics datasets for research or business purposes
In this project, we use Scikit-Learn, a popular Python library for building regression models
The concepts in this project can be extended to build neural networks using Tensor Flow, Keras, or other tools
Save this course
Save Regression using Scikit-Learn to your list so you can find it easily later:
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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 using Scikit-Learn with these
activities:
Organize and review course notes, assignments, and quizzes
Show steps
This activity will help you solidify your understanding of the course material by revisiting key concepts and reinforcing your knowledge.
Show steps
-
Gather all your course materials, including notes, assignments, and quizzes.
-
Review each material, focusing on the main concepts and takeaways.
-
Create summaries or mind maps to help you retain the information.
Review high school algebra
Show steps
Reviewing algebra and linear algebra will help you understand the concepts and notation used in machine learning and statistical modeling.
Browse courses on
Algebra
Show steps
-
Go over your high school algebra notes.
-
Take practice problems on functions, equations, and matrices.
-
Watch online tutorials on linear algebra and matrix theory.
Connect with experts in the field
Show steps
Connecting with experts in the field of machine learning will provide you with valuable insights, advice, and potential opportunities for collaboration.
Show steps
-
Attend industry events and meetups.
-
Reach out to professors, researchers, and professionals in the field.
-
Join online communities and forums.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Review 'An Introduction to Statistical Learning with Applications in R' by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
Show steps
Reviewing this book will provide a solid grounding in the fundamental concepts and techniques used in statistical learning, which is essential for understanding advanced regression models and machine learning algorithms.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read the book's introduction and first three chapters, focusing on understanding the key concepts of statistical learning and regression.
-
Work through the exercises in the book to practice applying the techniques described in the chapters.
-
Summarize the main takeaways from each chapter to reinforce understanding.
Follow tutorials on building regression models with Python
Show steps
Following tutorials will provide a structured approach to learning the practical aspects of building regression models using Python, complementing the theoretical knowledge gained in the course.
Browse courses on
Regression
Show steps
-
Search for tutorials that cover regression models in Python.
-
Select a tutorial that provides clear and concise explanations.
-
Work through the tutorial, following the instructions and implementing the code.
-
Experiment with different parameters and datasets to gain a deeper understanding of the models' behavior.
Follow online tutorials and workshops
Show steps
Following online tutorials and workshops will provide you with additional resources and guidance to enhance your understanding of the concepts covered in the course.
Browse courses on
Machine Learning
Show steps
-
Find reputable online tutorials and workshops.
-
Follow the tutorials and workshops step-by-step.
-
Take notes and complete any practice exercises provided.
Solve regression practice problems on Kaggle
Show steps
Solving practice problems on Kaggle will provide hands-on experience in applying regression models to real-world datasets, reinforcing the concepts learned in the course.
Browse courses on
Regression
Show steps
-
Identify a regression problem on Kaggle that aligns with your interests.
-
Download the dataset and explore it to understand the variables and their relationships.
-
Choose and apply appropriate regression models to the dataset.
-
Evaluate the performance of your models using relevant metrics.
Work through practice problems
Show steps
Working through practice problems will help you solidify your understanding of the algorithms, methods, and formulas used in regression modeling.
Browse courses on
Machine Learning
Show steps
-
Find practice problems online or in textbooks.
-
Work through the problems step-by-step.
-
Check your answers and identify areas where you need more practice.
Join a study group or discussion forum
Show steps
Joining a study group or discussion forum will provide you with opportunities to collaborate with other learners, share knowledge, and get help with challenging concepts.
Show steps
-
Join an online or in-person study group.
-
Participate in discussions and share your own insights.
-
Collaborate on projects or assignments.
Develop a regression model for a real-world problem
Show steps
Undertaking a project will allow you to apply the concepts and techniques learned in the course to a practical scenario, fostering a deeper understanding and solidifying the knowledge gained.
Browse courses on
Regression
Show steps
-
Identify a real-world problem that can be addressed using regression analysis.
-
Gather and clean the necessary data.
-
Choose and implement appropriate regression models.
-
Evaluate the performance of your models and select the best one.
-
Deploy your model and monitor its performance over time.
Build a regression model from scratch
Show steps
Building a regression model from scratch will require you to apply the concepts you learn in the course and demonstrate your understanding of the modeling process.
Browse courses on
Machine Learning
Show steps
-
Choose a dataset to work with.
-
Clean and prepare the data.
-
Select and train a regression model.
-
Evaluate the model's performance.
-
Write a report summarizing your results.
Write a report summarizing your findings from the regression project
Show steps
Writing a report will help you consolidate your understanding of the project, document your work, and communicate your findings effectively.
Browse courses on
Regression
Show steps
-
Outline the report, including the introduction, methods, results, discussion, and conclusion sections.
-
Write each section, providing a clear and concise summary of your project.
-
Proofread and edit your report carefully before submitting it.
Contribute to open-source projects
Show steps
Contributing to open-source projects will allow you to gain practical experience, collaborate with others, and stay up-to-date with the latest developments in the field.
Browse courses on
Machine Learning
Show steps
-
Find open-source projects related to machine learning.
-
Start contributing to the projects by submitting bug fixes, improvements, or new features.
-
Engage with other contributors and learn from their experiences.
Organize and review course notes, assignments, and quizzes
Show steps
This activity will help you solidify your understanding of the course material by revisiting key concepts and reinforcing your knowledge.
Show steps
Show steps
Show steps
- Gather all your course materials, including notes, assignments, and quizzes.
- Review each material, focusing on the main concepts and takeaways.
- Create summaries or mind maps to help you retain the information.
Review high school algebra
Show steps
Reviewing algebra and linear algebra will help you understand the concepts and notation used in machine learning and statistical modeling.
Browse courses on
Algebra
Show steps
Show steps
Show steps
- Go over your high school algebra notes.
- Take practice problems on functions, equations, and matrices.
- Watch online tutorials on linear algebra and matrix theory.
Connect with experts in the field
Show steps
Connecting with experts in the field of machine learning will provide you with valuable insights, advice, and potential opportunities for collaboration.
Show steps
Show steps
Show steps
- Attend industry events and meetups.
- Reach out to professors, researchers, and professionals in the field.
- Join online communities and forums.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Review 'An Introduction to Statistical Learning with Applications in R' by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
Show steps
Reviewing this book will provide a solid grounding in the fundamental concepts and techniques used in statistical learning, which is essential for understanding advanced regression models and machine learning algorithms.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read the book's introduction and first three chapters, focusing on understanding the key concepts of statistical learning and regression.
- Work through the exercises in the book to practice applying the techniques described in the chapters.
- Summarize the main takeaways from each chapter to reinforce understanding.
Follow tutorials on building regression models with Python
Show steps
Following tutorials will provide a structured approach to learning the practical aspects of building regression models using Python, complementing the theoretical knowledge gained in the course.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Search for tutorials that cover regression models in Python.
- Select a tutorial that provides clear and concise explanations.
- Work through the tutorial, following the instructions and implementing the code.
- Experiment with different parameters and datasets to gain a deeper understanding of the models' behavior.
Follow online tutorials and workshops
Show steps
Following online tutorials and workshops will provide you with additional resources and guidance to enhance your understanding of the concepts covered in the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find reputable online tutorials and workshops.
- Follow the tutorials and workshops step-by-step.
- Take notes and complete any practice exercises provided.
Solve regression practice problems on Kaggle
Show steps
Solving practice problems on Kaggle will provide hands-on experience in applying regression models to real-world datasets, reinforcing the concepts learned in the course.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Identify a regression problem on Kaggle that aligns with your interests.
- Download the dataset and explore it to understand the variables and their relationships.
- Choose and apply appropriate regression models to the dataset.
- Evaluate the performance of your models using relevant metrics.
Work through practice problems
Show steps
Working through practice problems will help you solidify your understanding of the algorithms, methods, and formulas used in regression modeling.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks.
- Work through the problems step-by-step.
- Check your answers and identify areas where you need more practice.
Join a study group or discussion forum
Show steps
Joining a study group or discussion forum will provide you with opportunities to collaborate with other learners, share knowledge, and get help with challenging concepts.
Show steps
Show steps
Show steps
- Join an online or in-person study group.
- Participate in discussions and share your own insights.
- Collaborate on projects or assignments.
Develop a regression model for a real-world problem
Show steps
Undertaking a project will allow you to apply the concepts and techniques learned in the course to a practical scenario, fostering a deeper understanding and solidifying the knowledge gained.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed using regression analysis.
- Gather and clean the necessary data.
- Choose and implement appropriate regression models.
- Evaluate the performance of your models and select the best one.
- Deploy your model and monitor its performance over time.
Build a regression model from scratch
Show steps
Building a regression model from scratch will require you to apply the concepts you learn in the course and demonstrate your understanding of the modeling process.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a dataset to work with.
- Clean and prepare the data.
- Select and train a regression model.
- Evaluate the model's performance.
- Write a report summarizing your results.
Write a report summarizing your findings from the regression project
Show steps
Writing a report will help you consolidate your understanding of the project, document your work, and communicate your findings effectively.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Outline the report, including the introduction, methods, results, discussion, and conclusion sections.
- Write each section, providing a clear and concise summary of your project.
- Proofread and edit your report carefully before submitting it.
Contribute to open-source projects
Show steps
Contributing to open-source projects will allow you to gain practical experience, collaborate with others, and stay up-to-date with the latest developments in the field.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find open-source projects related to machine learning.
- Start contributing to the projects by submitting bug fixes, improvements, or new features.
- Engage with other contributors and learn from their experiences.
Career center
Learners who complete Regression using Scikit-Learn will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their understanding of programming as well as advanced mathematics to find trends and patterns in data. They build machine learning models to help with tasks such as predicting customer behavior, identifying fraud, and improving product quality. This course can help you build a foundation in regression modeling, an important technique used by Data Scientists. As someone looking to break into the field, this course provides a solid start for you.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers utilize their knowledge of computer science and software engineering to build and maintain machine learning models. They collaborate closely with Data Scientists to ensure that models are deployed and operating as intended. This course introduces you to building regression models in Python using Scikit-Learn, a valuable tool for Machine Learning Engineers.
Statistical Analyst
Statistical Analyst
Statistical Analysts are responsible for collecting, analyzing, and interpreting data. They use statistical methods to draw conclusions about the underlying processes that generate data, providing insights to businesses and organizations. This course provides a strong foundation in regression analysis, which is widely used for uncovering relationships between variables.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts develop and use mathematical and analytical techniques to solve complex business problems. They leverage data to optimize processes, improve efficiency, and make better decisions. Regression modeling is a fundamental technique used by Operations Research Analysts to predict outcomes and make data-driven recommendations. The course can equip you with these skills.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They play a vital role in the financial industry. This course introduces regression modeling, a cornerstone technique for Quantitative Analysts, helping you understand the fundamentals and their application in financial contexts.
Market Researcher
Market Researcher
Market Researchers conduct research to understand consumer behavior and trends. They analyze data to identify opportunities and challenges for businesses. This course provides a solid foundation in regression analysis, a powerful technique used by Market Researchers to uncover relationships between market variables. By taking this course, you can enhance your understanding of customer behavior and make more informed recommendations.
Business Analyst
Business Analyst
Business Analysts help organizations improve their performance by analyzing data and identifying inefficiencies. They use various techniques, including regression analysis, to understand business processes and recommend solutions. This course provides a practical introduction to regression modeling, a valuable skill for Business Analysts seeking to enhance their analytical capabilities.
Data Analyst
Data Analyst
Data Analysts clean, analyze, and interpret data to extract meaningful insights. Regression modeling is a common technique used by Data Analysts to identify relationships between variables and make predictions. This course introduces you to the fundamentals of regression using Scikit-Learn, helping you develop the skills needed to excel as a Data Analyst.
Financial Analyst
Financial Analyst
Financial Analysts assess the financial health of companies and make investment recommendations. They use various techniques, including regression analysis, to analyze financial data and make informed decisions. This course provides an introduction to regression modeling, a fundamental skill for Financial Analysts. By taking this course, you can enhance your ability to analyze financial data and make sound investment decisions.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. They play a vital role in the insurance and finance industries. Regression analysis is a valuable technique used by Actuaries to model risk factors and make informed decisions. This course introduces you to regression modeling, providing a foundation for success in the actuarial field.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in the life sciences. They design and analyze studies, and interpret data to draw conclusions about biological processes. Regression analysis is a commonly used technique in biostatistics, and this course can help you build a solid foundation in the subject.
Risk Manager
Risk Manager
Risk Managers identify, assess, and mitigate risks for organizations. They use various techniques, including regression analysis, to quantify and manage risk. This course provides an introduction to regression modeling, a valuable tool for Risk Managers. By understanding the fundamentals of regression, you can enhance your ability to assess and manage risk effectively.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They may use regression modeling to evaluate the performance and reliability of software systems. While not directly related to the core responsibilities of a Software Engineer, this course can provide a useful introduction to regression modeling, which may be beneficial for those interested in developing software with predictive capabilities.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines to store, process, and analyze data. They may use regression modeling to clean and prepare data for analysis. While not a primary responsibility, this course can provide a basic understanding of regression modeling, which may be helpful for Data Engineers working with predictive analytics projects.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. They may use regression modeling to optimize database performance and identify potential issues. While not a core responsibility, this course can introduce the basics of regression modeling, providing a foundation for Database Administrators looking to expand their skillset in data analysis.
For more career information including salaries, visit:
OpenCourser.com/course/cb4s39/regression
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
Regression using Scikit-Learn.
A classic textbook that provides a comprehensive overview of statistical learning methods.
Save
A comprehensive and practical guide to predictive modeling with a focus on the R programming language.
Save
A comprehensive reference book on deep learning.
Save
A Chinese-language textbook on statistical machine learning that provides a rigorous mathematical treatment.
Save
A Chinese translation of the comprehensive reference book on deep learning.
A practical guide to machine learning with Python, focusing on real-world applications.
A practical guide to machine learning with scikit-learn, Keras, and TensorFlow, three popular Python libraries.
Provides hands-on experience with real-world machine learning projects using Python.
A comprehensive guide to natural language processing with Python.
A more accessible introduction to statistical learning, suitable for beginners.
A hands-on guide to data science, including machine learning, using Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cb4s39/regression
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Time
1 hours
Via
Coursera
Institution
Coursera Community Project Network
Instructor
PARTHA MAJUMDAR
Good to know
Teaches methods for developing predictive analytics datasets for research or business purposes
In this project, we use Scikit-Learn, a popular Python library for building regression models
The concepts in this project can be extended to build neural networks using Tensor Flow, Keras, or other tools
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