John Hopkins University - Advanced Linear Models for Data Science 1: Least Squares
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Advanced Linear Models for Data Science 1: Least Squares at Coursera Overview
Duration | 8 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Advanced Linear Models for Data Science 1: Least Squares at Coursera Highlights
- Earn a certificate from the university of Johns Hopkins upon completion of course.
- Flexible deadlines according to your schedule.
Advanced Linear Models for Data Science 1: Least Squares at Coursera Course details
- Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following:
- - A basic understanding of linear algebra and multivariate calculus.
- - A basic understanding of statistics and regression models.
- - At least a little familiarity with proof based mathematics.
- - Basic knowledge of the R programming language.
- After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.
Advanced Linear Models for Data Science 1: Least Squares at Coursera Curriculum
Background
Introduction
Matrix derivatives
Coding example
Centering by matrix multiplication
Coding example
Variance via matrix multiplication
Coding example
Welcome to the class
Course textbook
Grading
In this module
Background Quiz
One and two parameter regression
Regression through the origin
Centering first
Coding example
Connection with linear regression
Coding example
Fitted values and residuals
Before you begin
Before you begin
One Parameter Regression Quiz
Linear regression
Least squares
Coding example
Prediction
Coding example
Residuals
Coding example
Generalizations
Generalizations example
Before you begin
Generalizations
Linear Regression Quiz
General least squares
Least squares
Coding example
Second derivation of least squares
Projections
Third derivation of least squares
Coding example
Before you begin
General Least Squares Quiz
Least squares examples
Basic examples of design matrices and fits
Group effects
Change of parameterization
ANCOVA
Least Squares Examples Quiz
Bases and residuals
Bases, introduction
Bases 2, Fourier
Bases 3, SVDs
Bases, coding example
Introduction to residuals
Partitioning variability
Bases Quiz
Residuals Quiz
Advanced Linear Models for Data Science 1: Least Squares at Coursera Admission Process
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