Calculus/Linear Algebra programming in R tutor
March 26, 2018 6:10 AM
I am taking an online course called Linear Algebra and Calculus for Machine Learning, which is in preparation me to take a Machine Learning course in the same program. I have taken Calc 1 25 years ago in undergrad but am not very good at it. I am over my head in understanding how to apply Calculus to Linear Algebra (Taylor sequence, for example), and am a beginner in R programming. I would like help in understanding the reasoning behind the equations and matrices as well as how to do more complex R programming in the assignments. They aren't looking for expert code but rather just getting to the right answer using functions, loops if needed.
Start Date: August 21, 2018
End Date: October 9, 2018
This course is for students pursuing the Certification in Practice of Data Analytics but do not have the necessary background or education required for the Machine Learning course. Of if it has been some time since you completed your education and need a refresher course in linear algebra and calculus. It's completely optional and not required otherwise to receive the certification.
Course Description
This is a course on linear algebra and calculus for understanding machine learning algorithms. Majority of the course is designed at the level of second year undergraduate curriculum in engineering, but the last module applies the knowledge to solve simple machine learning tasks. The course covers vectors, matrices, matrix operations, eigenvalues and eigenvectors, principle component analysis, linear regression, simple classification and clustering algorithms. The course ends with a project in which the students are expected to implement the ideas studied in the course to solve a machine learning problem.
You will learn to:
Represent data in a vector or matrix format
Perform vector and matrix operations
Understand calculus of functions of multiple vectors
Apply knowledge of linear algebra and calculus for machine learning applications
Formulate machine learning tasks as optimization routines
learning
payscale: $25/half hour
job type: contract
Start Date: August 21, 2018
End Date: October 9, 2018
This course is for students pursuing the Certification in Practice of Data Analytics but do not have the necessary background or education required for the Machine Learning course. Of if it has been some time since you completed your education and need a refresher course in linear algebra and calculus. It's completely optional and not required otherwise to receive the certification.
Course Description
This is a course on linear algebra and calculus for understanding machine learning algorithms. Majority of the course is designed at the level of second year undergraduate curriculum in engineering, but the last module applies the knowledge to solve simple machine learning tasks. The course covers vectors, matrices, matrix operations, eigenvalues and eigenvectors, principle component analysis, linear regression, simple classification and clustering algorithms. The course ends with a project in which the students are expected to implement the ideas studied in the course to solve a machine learning problem.
You will learn to:
Represent data in a vector or matrix format
Perform vector and matrix operations
Understand calculus of functions of multiple vectors
Apply knowledge of linear algebra and calculus for machine learning applications
Formulate machine learning tasks as optimization routines
learning
payscale: $25/half hour
job type: contract