Feature Engineering Techniques for Machine Learning
Machine Learning
| Intermediate
- 12 videos | 1h 6m 51s
- Earns a Badge
Feature engineering is about thoughtfully selecting and creating features to boost the accuracy and reliability of machine learning (ML) models. In this course, learn how to select and create features that improve ML model accuracy and reliability, strategies to mitigate overfitting risks, the impact of well-chosen features on model performance, and how to craft new features to enhance predictive capabilities. Next, discover how to build scikit-learn pipelines, implement feature engineering techniques, and how creating polynomial features for regression models helps capture complex data patterns. Finally, explore how to apply log and power transformations and implement principal component analysis (PCA) for dimensionality reduction. After completing this course, you will be able to apply feature engineering techniques for machine learning.
WHAT YOU WILL LEARN
-
Discover the key concepts covered in this course
Identify how to transform raw data into meaningful ml input features
Outline feature engineering techniques for enhancing model performance and data representation
Utilize feature creation to enhance the predictive power of models
Analyze and transform raw training data using a preconfigured pipeline
Perform feature engineering by creating new features and handling null values -
Model polynomial features to improve fit quality in regression analysis
Apply feature engineering and polynomial features to build regression models
Implement log transformations to reduce skewness in data
Implement principal component analysis to reduce dimensionality in datasets
Apply power transformations to reduce skewness and improve data normality
Summarize the key concepts covered in this course
IN THIS COURSE
-
1m 5sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
-
4m 8sAfter completing this video, you will be able to identify how to transform raw data into meaningful ML input features. FREE ACCESS
-
3. Feature Creation and Model Impact8m 10sUpon completion of this video, you will be able to outline feature engineering techniques for enhancing model performance and data representation. FREE ACCESS
-
4. Synthesizing Features and Constructing Scikit-Learn Pipelines8m 59sLearn how to utilize feature creation to enhance the predictive power of models. FREE ACCESS
-
5. Executing Pipeline Fit, Transformations, and Model Evaluation6m 51sIn this video, find out how to analyze and transform raw training data using a preconfigured pipeline. FREE ACCESS
-
6. Performing Feature Engineering5m 12sDiscover how to perform feature engineering by creating new features and handling null values. FREE ACCESS
-
7. Modeling Non-Linear Relationships with Polynomial Features6m 55sIn this video, learn how to model polynomial features to improve fit quality in regression analysis. FREE ACCESS
-
8. Creating Polynomial Features for a Regression Model5m 48sFind out how to apply feature engineering and polynomial features to build regression models. FREE ACCESS
-
9. Performing Log Transformations on Features7m 32sDuring this video, discover how to implement log transformations to reduce skewness in data. FREE ACCESS
-
10. Implementing Principal Component Analysis6m 40sIn this video, you will learn how to implement principal component analysis to reduce dimensionality in datasets. FREE ACCESS
-
11. Applying Power Transformations and Performing PCA4m 13sFind out how to apply power transformations to reduce skewness and improve data normality. FREE ACCESS
-
12. Course Summary1m 19sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.
Digital badges are yours to keep, forever.
