We may earn an affiliate commission when you visit our partners.
Brandon Strain
Would you like to better understand the basics of linear algebra so that you can better understand the techniques used in regression and machine learning? In this course, Applying Linear Algebra with R, you will learn foundational knowledge to understand what...
Read more
Would you like to better understand the basics of linear algebra so that you can better understand the techniques used in regression and machine learning? In this course, Applying Linear Algebra with R, you will learn foundational knowledge to understand what...
Read more
Would you like to better understand the basics of linear algebra so that you can better understand the techniques used in regression and machine learning? In this course, Applying Linear Algebra with R, you will learn foundational knowledge to understand what is going on in predictive models, how to extract important information from large data sets, and the basics of linear regression in R. First, you will learn basic matrix arithmetic. Next, you will discover advanced matrix mathematics that will help build your foundation. Finally, you will explore how to put this math together into real world applications. When you are finished with this course, you will have the skills and knowledge of Linear Algebra in R needed to better implement basic machine learning techniques and springboard into more advanced topics like generalized linear models.
Register for this course and see more details by visiting:
OpenCourser.com/course/zalzo8/applying
Here's a deal for you
We found an offer that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for beginners who want to better understand foundational methods used in regression and machine learning
Develops basic matrix mathematics that will help build your foundation for multiple applications
Gives learners an edge in understanding innovation and groundbreaking topics in linear algebra
Explores linear algebra in R, which is standard in industry
Teaches skills, knowledge, and tools that are highly relevant to industry
Save this course
Save Applying Linear Algebra with R to your list so you can find it easily later:
Save
Save
Activities
Coming soon
We're preparing activities for
Applying Linear Algebra with R.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Applying Linear Algebra with R will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Many Data Scientists use linear algebra and regression to make sense of big data. This course could help you to build a foundation for understanding these techniques with an eye towards data science. In particular, the section covering how to put this math together into real world applications will be especially helpful.
Statistician
Statistician
Statisticians must know linear algebra for working with big data sets. This course focuses specifically on applying linear algebra to datasets which could be particularly valuable.
Machine Learning Engineer
Machine Learning Engineer
Linear algebra and regression are foundational topics in machine learning and many Machine Learning Engineers have these skills. While the course touches on these topics, a deeper dive into them would be needed to fully succeed as a Machine Learning Engineer. Still, this course could be a strong starting point for someone looking to work in this field.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems and linear regression and machine learning are often their tools for doing so. This course could help a Data Analyst in their foundational knowledge of these topics.
Operations Research Analyst
Operations Research Analyst
These professionals use machine learning and regression to understand and improve complex business processes. This course could be a solid starting block for someone looking to become an Operations Research Analyst.
Actuary
Actuary
Actuaries may need to work with regression and big data to calculate risk in insurance and pension plans. This course covers these topics and could help an aspiring actuary build a solid foundation.
Market Research Analyst
Market Research Analyst
Market Research Analysts use linear regression to analyze big datasets and understand consumer behavior. This course covers these topics and could help an aspiring analyst gain valuable skills.
Data Engineer
Data Engineer
Data Engineers prepare big data for use with machine learning and other analytic tools. This course could provide a solid foundation for someone in this role, especially the sections on matrix arithmetic.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use big data to help investment firms make better decisions. They frequently use regression and machine learning and this course could provide a useful introduction to these topics.
Business Analyst
Business Analyst
Business Analysts use data to help companies make better decisions. This course could help someone in this role understand how to apply linear regression and other techniques to big data.
Software Engineer
Software Engineer
Software Engineers often work with data and may need to use linear regression or machine learning to build features or train models. This course could provide valuable foundational knowledge to someone in this role.
Risk Manager
Risk Manager
Risk Managers use data to assess and mitigate risk. This course could provide valuable knowledge to someone in this role, especially the section on linear regression in R. which can help to analyze and predict risk.
Database Administrator
Database Administrator
Database Administrators maintain and optimize databases, which often requires working with big data. This course could provide valuable knowledge to someone in this role, especially the section on extracting important information from large data sets.
Financial Analyst
Financial Analyst
This course may be useful for Financial Analysts, as many use linear regression to better understand the stock market and make predictions. While the focus of this course is not finance, the foundational linear algebra knowledge it provides could be helpful to a Financial Analyst.
Product Manager
Product Manager
Product Managers often work with data to understand customer needs and improve products and services. This course could provide useful foundational knowledge, especially the sections on matrix arithmetic and extracting information from large datasets.
For more career information including salaries, visit:
OpenCourser.com/course/zalzo8/applying
Reading list
We've selected 14 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
Applying Linear Algebra with R.
Save
Provides a comprehensive introduction to matrix computations, with a focus on algorithms and their implementation. It valuable resource for students who want to learn how to use linear algebra to solve computational problems.
Provides a comprehensive introduction to numerical linear algebra, with a focus on the theory and implementation of algorithms. It valuable resource for students who want to learn how to use linear algebra to solve computational problems.
This textbook provides a thorough treatment of linear algebra, with a focus on applications in engineering and the sciences. It valuable resource for students who want to learn how to use linear algebra to solve real-world problems.
This classic textbook provides a comprehensive introduction to linear algebra, covering the basics as well as advanced topics such as eigenvalues and eigenvectors. It valuable resource for anyone who wants to deepen their understanding of linear algebra.
Provides a comprehensive introduction to applied linear statistical models, with a focus on the use of R. It valuable resource for students who want to learn how to use linear algebra to model and analyze data.
Provides a comprehensive introduction to statistical learning, with a focus on the use of R. It valuable resource for students who want to learn how to use linear algebra to model and analyze data.
Provides a comprehensive introduction to statistical learning, with a focus on the use of R. It valuable resource for students who want to learn how to use linear algebra to model and analyze data.
Provides a comprehensive introduction to machine learning, with a focus on the use of R. It valuable resource for students who want to learn how to use linear algebra to model and analyze data.
Provides a comprehensive introduction to deep learning, with a focus on the use of R. It valuable resource for students who want to learn how to use linear algebra to model and analyze data.
Provides a comprehensive introduction to reinforcement learning, with a focus on the use of R. It valuable resource for students who want to learn how to use linear algebra to model and analyze data.
Save
Provides a comprehensive introduction to natural language processing, with a focus on the use of R. It valuable resource for students who want to learn how to use linear algebra to model and analyze data.
This introductory textbook provides a clear and concise overview of linear algebra. It good choice for students who are new to the subject or who need a refresher.
This textbook provides a comprehensive introduction to linear algebra, with a focus on applications in business and economics. It good choice for students who are interested in using linear algebra to model and solve business problems.
Provides a comprehensive introduction to linear models, with a focus on the use of R. It valuable resource for students who want to learn how to use linear algebra to model and analyze data.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zalzo8/applying
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Time
1 hours
Via
Pluralsight
Instructor
Brandon Strain
This course belongs to a
collection
called
Building Statistical and Mathematical Models with R. It's comprised of nine courses:
- Understanding Statistical Models and Mathematical Models
- Solving Problems with Numerical Methods
- Applying the Mathematical MASS Model with R
- Applying Differential Equations and Inverse Models with R
- Performing Dimension Analysis with R
- Applying Linear Algebra with R (viewing)
- Building Statistical Summaries with R
- Implementing Bootstrap Methods in R
- Implementing Monte Carlo Method in R
Good to know
Builds a strong foundation for beginners who want to better understand foundational methods used in regression and machine learning
Develops basic matrix mathematics that will help build your foundation for multiple applications
Gives learners an edge in understanding innovation and groundbreaking topics in linear algebra
Explores linear algebra in R, which is standard in industry
Teaches skills, knowledge, and tools that are highly relevant to industry
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser