Machine Learning
159 learners
Dimensionality Reduction with Feature Selection
In this course, you'll learn specialized techniques for feature selection and extraction to improve machine learning models. Through practical applications on a synthetic dataset, you'll discover how to identify and remove low-variance features, use correlation with the target variable, and apply advanced selection methods to refine your datasets for optimal efficiency and effectiveness.
MatPlotLib
Numpy
Pandas
Scikit-learn
See path
5 lessons
19 practices
4 hours
Badge for Feature Engineering,
Feature Engineering
Lessons and practices
Lesson 1: Mastering Variance-Based Feature Selection with VarianceThreshold in Python
Unveiling High Variance Features in Synthetic Data
Adjusting the Variance Threshold
Setting the Variance Threshold
Cosmic Code Crafting: Feature Selection with Variance Threshold
Lesson 2: Unveiling the Power of Univariate Feature Selection with SelectKBest in Python
Unveiling the Most Informative Features with Chi-Square Test
Expanding Our Feature Universe
Uncovering the Stars: Selecting Features with Chi-Square
Implementing SelectKBest for Feature Selection
Lesson 3: Mastering Feature Selection with Mutual Information in Python
Visualizing Wine Data with Mutual Information
Refining Feature Selection with SelectPercentile
Computing Mutual Relationships in Features
Wine Dataset Feature Selection with Mutual Information
Lesson 4: Mastering Feature Selection with Recursive Feature Elimination in Python
Unveiling the Top Features with Recursive Feature Elimination
Adjusting Feature Selection with RFE
Navigating the Stars of Feature Selection
Navigating the Stars: Recursive Feature Elimination
Lesson 5: Mastering Feature Selection with SelectFromModel in Scikit-learn
Revealing Key Features in California Housing Prices
Adjusting Feature Selection Threshold
Implanting SelectFromModel in the Voyage of Feature Selection
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