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Ashish Dikshit
In this 1-hour long project-based course, you will learn how to
Train SVM regression model- with large & small margin, second degree polynomial kernel, make prediction using Linear SVM classifier; how a small weight vector results in a large margin? and finally
pictorial representation for Hinge loss. This project gives you easy access to the invaluable learning techniques used by experts in machine learning.
Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your understanding to thoroughness in machine learning.
Register for this course and see more details by visiting:
OpenCourser.com/course/5svb13/svm
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides in-depth training on SVM regression models with large and small margins, as well as second degree polynomial kernels
Offers practical guidance on making predictions using Linear SVM classifiers
Explains how a small weight vector results in a large margin
Provides a pictorial representation of Hinge loss for enhanced understanding
Facilitates easy access to expert-utilized machine learning techniques
Emphasizes the importance of changing thinking and understanding to achieve mastery in machine learning
Save this course
Save SVM Regression, prediction and losses 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 SVM Regression, prediction and losses with these
activities:
Re-learn polynomial equations
Show steps
Ensure that you have a strong grasp on polynomial equations before starting the course.
Show steps
-
Review your notes or textbooks on polynomial equations.
-
Practice solving polynomial equations, starting with simple equations and then moving on to more complex ones.
-
Identify the different methods of solving polynomial equations and practice using each one.
-
Solve polynomial equations that involve complex numbers.
-
Apply your knowledge of polynomial equations to solve real-world problems.
Follow SVM regression tutorials
Show steps
Supplement your understanding of SVM regression by following online tutorials.
Show steps
-
Find a reputable online tutorial on SVM regression.
-
Follow the tutorial step-by-step, making sure to understand each concept before moving on to the next.
-
Practice implementing the concepts you learn in the tutorial on your own.
-
Experiment with different SVM regression parameters to see how they affect the results.
-
Apply your knowledge of SVM regression to solve real-world problems.
Show all two activities
Re-learn polynomial equations
Show steps
Ensure that you have a strong grasp on polynomial equations before starting the course.
Show steps
Show steps
Show steps
- Review your notes or textbooks on polynomial equations.
- Practice solving polynomial equations, starting with simple equations and then moving on to more complex ones.
- Identify the different methods of solving polynomial equations and practice using each one.
- Solve polynomial equations that involve complex numbers.
- Apply your knowledge of polynomial equations to solve real-world problems.
Follow SVM regression tutorials
Show steps
Supplement your understanding of SVM regression by following online tutorials.
Show steps
Show steps
Show steps
- Find a reputable online tutorial on SVM regression.
- Follow the tutorial step-by-step, making sure to understand each concept before moving on to the next.
- Practice implementing the concepts you learn in the tutorial on your own.
- Experiment with different SVM regression parameters to see how they affect the results.
- Apply your knowledge of SVM regression to solve real-world problems.
Show all two activities
Career center
Learners who complete SVM Regression, prediction and losses will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This SVM Regression course provides a thorough understanding of the principles and techniques used in SVM regression, which is a widely used algorithm in machine learning. By gaining expertise in SVM regression, learners can develop and implement robust machine learning solutions, making them highly sought-after in the field of Machine Learning Engineering.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This SVM Regression course provides a solid understanding of SVM regression, a robust technique for modeling financial data. By mastering SVM regression, learners can develop more accurate predictive models and gain a competitive edge in the field of Quantitative Finance.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to extract meaningful insights. This SVM Regression course provides a practical understanding of SVM regression, a powerful technique for modeling and predicting continuous variables. By mastering SVM regression, learners can become more proficient in data analysis and contribute to decision-making processes within organizations.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess and manage financial risks. This SVM Regression course provides a strong foundation in the application of SVM regression, a technique for modeling and predicting financial outcomes. By mastering SVM regression, learners can develop more accurate actuarial models and contribute to sound financial planning.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and manage risks in various fields such as finance, insurance, and healthcare. This SVM Regression course provides a practical understanding of SVM regression, a powerful technique for modeling and predicting risk. By mastering SVM regression, learners can develop more accurate risk models and contribute to effective risk management strategies.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, interpret, and present data. This SVM Regression course provides a strong foundation in the application of SVM regression, a widely used technique in statistics. By gaining expertise in SVM regression, learners can enhance their statistical modeling skills and contribute to data-driven decision-making in various fields.
Econometrician
Econometrician
Econometricians use statistical methods to analyze economic data and develop economic models. This SVM Regression course provides a practical understanding of SVM regression, a robust technique for modeling economic relationships. By mastering SVM regression, learners can contribute to the development of more accurate economic models and forecasts.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This SVM Regression course provides a solid foundation in the application of SVM regression, a technique for modeling and optimizing systems. By mastering SVM regression, learners can develop more efficient and effective solutions for business operations.
Financial Analyst
Financial Analyst
Financial Analysts use financial data and analytical techniques to make investment decisions. This SVM Regression course provides a strong foundation in the application of SVM regression, a technique for modeling and predicting financial markets. By mastering SVM regression, learners can develop more accurate financial models and gain a competitive edge in the field of Finance.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to data in the field of biology and medicine. This SVM Regression course provides a practical understanding of SVM regression, a powerful technique for analyzing biological and medical data. By mastering SVM regression, learners can contribute to the development of new drugs, treatments, and personalized medicine.
Data Scientist
Data Scientist
Data Scientists play a significant role in making sense of large and complex datasets, using statistical and machine learning techniques. This SVM Regression course provides a solid foundation for understanding the concepts of SVM regression, prediction, and losses, which are crucial skills for Data Scientists to possess. By mastering these concepts, learners can enhance their ability to model and analyze data effectively, making them more valuable in the field of Data Science.
Business Analyst
Business Analyst
Business Analysts use data and analytical techniques to solve business problems and improve decision-making. This SVM Regression course provides a practical understanding of SVM regression, a powerful technique for modeling and predicting business outcomes. By mastering SVM regression, learners can develop more accurate business models and contribute to data-driven decision-making.
Market Researcher
Market Researcher
Market Researchers collect, analyze, and interpret data to understand consumer behavior and trends. This SVM Regression course provides a practical understanding of SVM regression, a powerful technique for modeling and predicting consumer behavior. By mastering SVM regression, learners can develop more accurate market research models and contribute to effective marketing strategies.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. This SVM Regression course provides a foundation in the application of SVM regression, a technique for modeling and predicting data. By mastering SVM regression, learners can develop more efficient and effective data engineering solutions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This SVM Regression course may be useful for Software Engineers who are interested in developing machine learning applications. By mastering SVM regression, learners can develop more accurate and efficient machine learning models, enhancing the performance of software applications.
For more career information including salaries, visit:
OpenCourser.com/course/5svb13/svm
Reading list
We've selected nine 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
SVM Regression, prediction and losses.
Provides a comprehensive overview of SVM regression, and valuable reference for researchers in the field.
Save
Provides a comprehensive overview of the field of machine learning, and includes coverage of SVM regression.
Provides a comprehensive overview of the field of machine learning, and includes coverage of SVM regression.
Provides a comprehensive overview of the field of machine learning, and includes coverage of SVM regression.
Provides a practical guide to machine learning, and includes clear explanations of SVM regression.
Provides a comprehensive overview of the field of machine learning, and includes coverage of SVM regression.
Provides a comprehensive overview of the field of machine learning, and includes coverage of SVM regression.
Save
Provides a comprehensive overview of the field of deep learning, and includes coverage of SVM regression.
Provides a comprehensive overview of the field of machine learning, and includes coverage of SVM regression.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5svb13/svm
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Ashish Dikshit
Good to know
Provides in-depth training on SVM regression models with large and small margins, as well as second degree polynomial kernels
Offers practical guidance on making predictions using Linear SVM classifiers
Explains how a small weight vector results in a large margin
Provides a pictorial representation of Hinge loss for enhanced understanding
Facilitates easy access to expert-utilized machine learning techniques
Emphasizes the importance of changing thinking and understanding to achieve mastery in machine learning
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