[FreeTutorials Us] Udemy Feature Engineering for Machine Learning Torrent Download Locations

[FreeTutorials.Us] Udemy - Feature Engineering for Machine Learning
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          How you can help Team-FTU.txt -

    1. Introduction
          1. Introduction.mp4 -

          1. Introduction.srt -

          1. Introduction.vtt -

          2. Course curriculum overview.mp4 -

          2. Course curriculum overview.srt -

          2. Course curriculum overview.vtt -

          3. Course requirements.mp4 -

          3. Course requirements.srt -

          3. Course requirements.vtt -

          4. How to approach this course.html -

          5. Setting up your computer.html -

          6. Download Jupyter notebooks.html -

          6.1 HandsOnPythonCode.zip.zip -

          7. Download datasets.html -

          8. Download course presentations.html -

          8.1 FeatureEngineeringSlides.zip.zip -

          9. FAQ Data Science, Python programming, datasets, presentations and more....html -

    10. Feature Scaling
          1. Feature scaling Introduction.mp4 -

          1. Feature scaling Introduction.srt -

          1. Feature scaling Introduction.vtt -

          10. Scaling to median and quantiles.mp4 -

          10. Scaling to median and quantiles.srt -

          10. Scaling to median and quantiles.vtt -

          11. Robust Scaling Demo.mp4 -

          11. Robust Scaling Demo.srt -

          11. Robust Scaling Demo.vtt -

          12. Scaling to vector unit length.mp4 -

          12. Scaling to vector unit length.srt -

          12. Scaling to vector unit length.vtt -

          13. Scaling to vector unit length Demo.mp4 -

          13. Scaling to vector unit length Demo.srt -

          13. Scaling to vector unit length Demo.vtt -

          14. Additional reading resources.html -

          2. Standardisation.mp4 -

          2. Standardisation.srt -

          2. Standardisation.vtt -

          3. Standardisation Demo.mp4 -

          3. Standardisation Demo.srt -

          3. Standardisation Demo.vtt -

          4. Mean normalisation.mp4 -

          4. Mean normalisation.srt -

          4. Mean normalisation.vtt -

          5. Mean normalisation Demo.mp4 -

          5. Mean normalisation Demo.srt -

          5. Mean normalisation Demo.vtt -

          6. Scaling to minimum and maximum values.mp4 -

          6. Scaling to minimum and maximum values.srt -

          6. Scaling to minimum and maximum values.vtt -

          7. MinMaxScaling Demo.mp4 -

          7. MinMaxScaling Demo.srt -

          7. MinMaxScaling Demo.vtt -

          8. Maximum absolute scaling.mp4 -

          8. Maximum absolute scaling.srt -

          8. Maximum absolute scaling.vtt -

          9. MaxAbsScaling Demo.mp4 -

          9. MaxAbsScaling Demo.srt -

          9. MaxAbsScaling Demo.vtt -

    11. Engineering mixed variables
          1. Engineering mixed variables.mp4 -

          1. Engineering mixed variables.srt -

          1. Engineering mixed variables.vtt -

          2. Engineering mixed variables Demo.mp4 -

          2. Engineering mixed variables Demo.srt -

          2. Engineering mixed variables Demo.vtt -

    12. Engineering datetime variables
          1. Engineering datetime variables.mp4 -

          1. Engineering datetime variables.srt -

          1. Engineering datetime variables.vtt -

          2. Engineering dates Demo.mp4 -

          2. Engineering dates Demo.srt -

          2. Engineering dates Demo.vtt -

          3. Engineering time variables and different timezones.mp4 -

          3. Engineering time variables and different timezones.srt -

          3. Engineering time variables and different timezones.vtt -

    13. Assembling a feature engineering pipeline
          1. Classification pipeline.mp4 -

          1. Classification pipeline.srt -

          1. Classification pipeline.vtt -

          2. Regression pipeline.mp4 -

          2. Regression pipeline.srt -

          2. Regression pipeline.vtt -

          3. Beat the performance by engineering features.html -

    14. Final section Next steps
          1. BONUS Discounts on my other courses!.html -

    2. Variable Types
          1. Variables Intro.mp4 -

          1. Variables Intro.srt -

          1. Variables Intro.vtt -

          2. Numerical variables.mp4 -

          2. Numerical variables.srt -

          2. Numerical variables.vtt -

          3. Categorical variables.mp4 -

          3. Categorical variables.srt -

          3. Categorical variables.vtt -

          4. Date and time variables.mp4 -

          4. Date and time variables.srt -

          4. Date and time variables.vtt -

          5. Mixed variables.mp4 -

          5. Mixed variables.srt -

          5. Mixed variables.vtt -

          5.1 sample_s2.csv.csv -

          6. Bonus More about the Lending Club dataset.html -

          7. Quiz about variable types.html -

    3. Variable Characteristics
          1. Variable characteristics.mp4 -

          1. Variable characteristics.srt -

          1. Variable characteristics.vtt -

          10. Bonus Additional reading resources.html -

          11. FAQ How can I learn more about machine learning.html -

          2. Missing data.mp4 -

          2. Missing data.srt -

          2. Missing data.vtt -

          3. Cardinality - categorical variables.mp4 -

          3. Cardinality - categorical variables.srt -

          3. Cardinality - categorical variables.vtt -

          4. Rare Labels - categorical variables.mp4 -

          4. Rare Labels - categorical variables.srt -

          4. Rare Labels - categorical variables.vtt -

          5. Linear models assumptions.mp4 -

          5. Linear models assumptions.srt -

          5. Linear models assumptions.vtt -

          6. Variable distribution.mp4 -

          6. Variable distribution.srt -

          6. Variable distribution.vtt -

          7. Outliers.mp4 -

          7. Outliers.srt -

          7. Outliers.vtt -

          8. Variable magnitude.mp4 -

          8. Variable magnitude.srt -

          8. Variable magnitude.vtt -

          9. Bonus Machine learning algorithms overview.html -

          9.1 ML_Comparison.pdf.pdf -

    4. Missing Data Imputation
          1. Introduction to missing data imputation.mp4 -

          1. Introduction to missing data imputation.srt -

          1. Introduction to missing data imputation.vtt -

          10. Mean or median imputation with Scikit-learn.mp4 -

          10. Mean or median imputation with Scikit-learn.srt -

          10. Mean or median imputation with Scikit-learn.vtt -

          11. Arbitrary value imputation with Scikit-learn.mp4 -

          11. Arbitrary value imputation with Scikit-learn.srt -

          11. Arbitrary value imputation with Scikit-learn.vtt -

          12. Frequent category imputation with Scikit-learn.mp4 -

          12. Frequent category imputation with Scikit-learn.srt -

          12. Frequent category imputation with Scikit-learn.vtt -

          13. Missing category imputation with Scikit-learn.mp4 -

          13. Missing category imputation with Scikit-learn.srt -

          13. Missing category imputation with Scikit-learn.vtt -

          14. Adding a missing indicator with Scikit-learn.mp4 -

          14. Adding a missing indicator with Scikit-learn.srt -

          14. Adding a missing indicator with Scikit-learn.vtt -