Feature Selection and Dimensionality Reduction
Machine Learning
| Intermediate
- 12 videos | 1h 11m 29s
- Earns a Badge
Feature selection and dimensionality reduction are key techniques in machine learning (ML) that streamline data complexity and enhance model performance. In this course, learn various feature selection methods for ML data, the approaches and advantages of these methods, and the importance of dimensionality reduction. Next, examine multicollinearity in your data, implement feature selection using the variance threshold and the f-statistic techniques, use mutual information regression to select features, and explore model-based feature selection using ridge regression for multicollinearity. Finally, explore sequential feature selection, perform feature selection for classification tasks using chi-squared and the f-statistic, and execute recursive feature selection. After completing this course, you will be able to execute feature selection and dimensionality reduction.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this course
List feature selection techniques for enhancing model accuracy and interpretability
Outline feature selection techniques, including the filter, wrapper, and embedded methods
Recognize dimensionality reduction techniques like principal component analysis (pca) and t-sne
Utilize various techniques to select optimal regression model features
Employ the variance threshold and f-regression techniques for feature selection in regression -
Apply mutual information regression to improve feature selection accuracy
Employ the ridgecv model for model-based feature selection and visualization
Use sequential feature selection techniques for model optimization
Perform feature selection using a chi-squared test in a classification problem
Apply feature selection techniques for classification using statistical methods and recursive feature elimination
Summarize the key concepts covered in this course
IN THIS COURSE
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1m 10sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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6m 10sUpon completion of this video, you will be able to list feature selection techniques for enhancing model accuracy and interpretability. FREE ACCESS
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3. Feature Selection Techniques7m 56sAfter completing this video, you will be able to outline feature selection techniques, including the filter, wrapper, and embedded methods. FREE ACCESS
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4. Dimensionality Reduction with Principal Component Analysis (PCA)7m 10sThrough this video, you will be able to recognize dimensionality reduction techniques like principal component analysis (PCA) and t-SNE. FREE ACCESS
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5. Analyzing Multicollinearity and Performing Feature Selection7m 27sDiscover how to utilize various techniques to select optimal regression model features. FREE ACCESS
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6. Performing Feature Selection Using Variance Threshold and the F-Statistic10m 19sIn this video, find out how to employ the variance threshold and f-regression techniques for feature selection in regression. FREE ACCESS
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7. Applying Mutual Information Regression to Enhance Feature Selection2m 35sLearn how to apply mutual information regression to improve feature selection accuracy. FREE ACCESS
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8. Implementing Model-Based Feature Selection with Ridge Regression6m 6sIn this video, discover how to employ the RidgeCV model for model-based feature selection and visualization. FREE ACCESS
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9. Executing Sequential Feature Selection4m 8sFind out how to use sequential feature selection techniques for model optimization. FREE ACCESS
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10. Performing Classification Feature Selection Using Chi-Squared and F-Statistic10m 11sIn this video, you will learn how to perform feature selection using a chi-squared test in a classification problem. FREE ACCESS
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11. Executing Recursive Feature Selection7m 4sDuring this video, discover how to apply feature selection techniques for classification using statistical methods and recursive feature elimination. FREE ACCESS
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12. Course Summary1m 16sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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