- How to download and install R
- How to install a package and import a library
- How to import a data (Formats : csv, txt) and how to set a working directory
- Eliminate duplicate rows
- Missing values detection and treatment
- Data visualization (Detection of Strongly correlated variables)
- select a subset of the data based on specified criteria
- Operation on columns, Variables and standardization , how to use the apply() f
- Selecting the number of principal components
- Computation of the correlation matrix, eigenvalues and vectors
- Computation of components
In this course, we learn the following:
How to Stet a working directory
How to Import a txt or csv file
How to eliminate duplicate rows in the data
How to detect rows containing missing values
How to eliminate rows containing missing values
How to replace missing values
How to select a subset of the data based on specifics criteria
How to do arithmetic on columns
How detect strongly correlated variable (some nice plots for visualization )
How to compute the correlation matrix , the eigenvalue and eigenvector
How select the number of components
How to compute the components
Learn to make Beaded Baseball Cap Key chain and a Beaded Bow Ring
in this course you'll be advice on living tall from a guy who is 6ft 8in
Learn Basics of Calculus (Mathematics) for Artificial Intelligence, Machine Learning and Data Science
About the instructors
- 3.76 Calificación
- 8764 Estudiantes
- 4 Cursos
Data Scientist, Statistician
Phd Student in Computer Science, My education Include a BS in Mathematics and a MS in Mathematical Statistics. I invest a lot of time on learning and teaching. Covering a wide range of topics in Mathematics, Statistics and Computer Science , Some of my main interests include machine learning, data reduction techniques, Statistical Computing, regression analysis and a wide range of mathematical Statistics topics including parameter estimate.
Join my courses and learn !!!!!
Thanks for the content of this course. I am using multispectral remote sensing data and at the same time, I am developing a machine learning tool based on them. Therefore, it was better for me to reduce the dimension of my data prior to run my prediction model. Obviously, the PCA is the integrated part of dimension reduction that widely used for satellite data and this course clearly learned me to calculate my PCAs for further analysis. I do appreciate the instructor for building this course
Something more on PCA plots should be included in the course.
Yes the content was good.
I was frustrated by the lack of explanations of the core principles and I felt the lectures did little more than state what each code line does but I am grateful that the teacher has put his time into making this R course available.
Express and fast expression of the technique. Clears the basic concept to support further reading