Building Features from Numeric Data

سرفصل های دوره

This course exhaustively covers data preprocessing techniques and transforms available in scikit-learn, allowing the construction of highly optimized features that are scaled, normalized and transformed in mathematically sound ways to fully harness the power of machine learning techniques.

1. Course Overview

1. Course Overview

2. Using Numeric Data in Machine Learning Algorithms

01. Version Check

02. Module Overview

03. Prerequisites and Course Outline

04. Scaling and Standardization

05. Mean, Variance, and Standard Deviation

06. Understanding Variance

07. Demo- Calculating Mean, Variance, and Standard Deviation

08. Demo- Box Plot Visualization and Data Standardization

09. Standard Scaler

10. Demo- Standardize Data Using the Scale Function

11. Demo- Standardize Data Using the Standard Scalar Estimator and Apply Bessels Correction

12. Robust Scaler

13. Demo- Scaling Data Using the Robust Scaler

14. Summary

3. Building Features Using Normalization

1. Module Overview

2. What Is Normalization-

3. Normalization and Cosine Similarity

4. Demo- Cosine Similarity and the L2 Norm

5. Demo- Normalizing Data to Simplify Cosine Similarity Calculations

6. Demo- K-means Clustering with Cosine Similarity

7. L1, L2 and Max Norms

8. Demo- Normalization Using L1, L2 and Max Norms

9. Summary

4. Building Features Using Scaling and Transformations

01. Module Overview

02. Converting Continuous Data to Categorical

03. Demo- Convert Numeric Data to Binary Categories Using a Binarizer

04. Demo- Using the KBinsDiscretizer to Categorize Numeric Values

05. Demo- Using Bin Values to Flag Outliers

06. Scaling Data

07. Demo- Scaling with the MaxAbsScaler

08. Demo- Scaling with the MinMaxScaler

09. Custom Transformations

10. Demo- Performing Custom Transforms Using the FunctionTransformer

11. Generating Polynomial Features

12. Demo- Using Polynomial Features to Transform Data

13. Transforming Features to Gaussian-like Distributions Using Power Transformers

14. Demo- Working with Chi Squared Distributed Input Features

15. Demo- Applying Power Transformers to Get Normal Distributions

16. Transforming Data to Normal or Uniform Distributions Using Quantile Transformers

17. Demo- Tranforming to a Normal Distribution Using the QuantileTransformer

18. Summary and Further Study

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تولید کننده: PluralSight