New to machine learning? This is the place to start: Linear regression, Logistic regression & Cluster Analysis
Instructor365 CareersEditorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
An instructor introduces three fundamental machine learning methods, listing linear regression, logistic regression, and clustering on screen.
An instructor presents two packages representing the core libraries used in this course: Scikit-learn on the left and StatsModels (SM) on the right.
The instructor discusses the balance between theoretical concepts and practical application in machine learning, illustrated by graphics on either side.
An instructor presents an overview of the course materials, highlighting two machine learning packages, three methods, dozens of videos, exercises, and extra resources.
The curriculum provides depth on Ordinary Least Squares (OLS) assumptions, covering linearity, endogeneity, normality, homoscedasticity, autocorrelation, and multicollinearity.
Instruction contrasts the statistical summary capabilities of StatsModels with the training and testing workflows found in Scikit-learn.
Learner signals indicate the installation environment may require updates, noting that modern Anaconda versions integrate libraries differently than shown in the videos.
The depth of statistical theory may be difficult for learners who lack a prior foundation in regression mathematics.
While targeting beginners, the depth of statistical theory may exceed the background of those without prior regression knowledge.
