Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more.
InstructorSoledad GalliThe curriculum covers a wide range of selection categories including Filter, Wrapper, Embedded, and Hybrid methods.
Instruction includes specific statistical measures such as Mutual Information, Chi-square, and Anova for feature evaluation.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This educational slide defines feature selection as the process of choosing relevant variables for machine learning model building, illustrated by a diagram of features flowing into a central process.
This diagram illustrates the diverse sources used for feature selection, including Kaggle competitions, scientific articles, business practice, Scikit Learn, and the internet.
This slide outlines the course audience and difficulty levels, targeting data scientists, analysts, and software developers across beginner, intermediate, and advanced skill tiers.
This slide outlines key course benefits including over eight hours of video lectures, lifetime access to materials, ready-to-use code examples, and global insights.
Learner signals indicate a desire for more dedicated code-along notebook structures rather than standalone lectures.
One signal suggests that some links to GitHub resources may be non-functional.
The curriculum aligns well with the technical requirements of data science, though it assumes a level of programming proficiency.