FreeTutorials Us Udemy Feature Engineering for Machine Learning | C4069CAC192C286F32CBE87A76FF1DDC6F293EA8

FreeTutorials Us Udemy Feature Engineering for Machine Learning

Name	[FreeTutorials.Us] Udemy - Feature Engineering for Machine Learning	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	4.0 GB
Total Files	338
Last Seen	2025-05-20 23:39
Hash	C4069CAC192C286F32CBE87A76FF1DDC6F293EA8

/0. Websites you may like/
0. (1Hack.Us) Premium Tutorials-Guides-Articles & Community based Forum.url	0.4 KB
1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url	0.3 KB
2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url	0.3 KB
3. (NulledPremium.com) Download E-Learning, E-Books, Audio-Books, Comics, Articles and more... etc.url	0.2 KB
4. (FTUApps.com) Download Cracked Developers Applications For Free.url	0.2 KB
How you can help Team-FTU.txt	0.2 KB
/1. Introduction/
1. Introduction.mp4	34.5 MB
1. Introduction.srt	7.0 KB
1. Introduction.vtt	6.2 KB
2. Course curriculum overview.mp4	35.0 MB
2. Course curriculum overview.srt	7.4 KB
2. Course curriculum overview.vtt	6.6 KB
3. Course requirements.mp4	11.2 MB
3. Course requirements.srt	4.2 KB
3. Course requirements.vtt	3.7 KB
4. How to approach this course.html	1.8 KB
5. Setting up your computer.html	3.6 KB
6. Download Jupyter notebooks.html	1.3 KB
6.1 HandsOnPythonCode.zip.zip	9.7 MB
7. Download datasets.html	2.0 KB
8. Download course presentations.html	0.8 KB
8.1 FeatureEngineeringSlides.zip.zip	31.0 MB
9. FAQ Data Science, Python programming, datasets, presentations and more....html	1.7 KB
/10. Feature Scaling/
1. Feature scaling Introduction.mp4	21.6 MB
1. Feature scaling Introduction.srt	4.7 KB
1. Feature scaling Introduction.vtt	4.2 KB
10. Scaling to median and quantiles.mp4	13.6 MB
10. Scaling to median and quantiles.srt	3.2 KB
10. Scaling to median and quantiles.vtt	2.9 KB
11. Robust Scaling Demo.mp4	17.4 MB
11. Robust Scaling Demo.srt	2.5 KB
11. Robust Scaling Demo.vtt	2.2 KB
12. Scaling to vector unit length.mp4	33.5 MB
12. Scaling to vector unit length.srt	6.8 KB
12. Scaling to vector unit length.vtt	6.0 KB
13. Scaling to vector unit length Demo.mp4	48.6 MB
13. Scaling to vector unit length Demo.srt	6.2 KB
13. Scaling to vector unit length Demo.vtt	5.5 KB
14. Additional reading resources.html	1.4 KB
2. Standardisation.mp4	27.8 MB
2. Standardisation.srt	6.7 KB
2. Standardisation.vtt	6.0 KB
3. Standardisation Demo.mp4	43.6 MB
3. Standardisation Demo.srt	5.6 KB
3. Standardisation Demo.vtt	5.0 KB
4. Mean normalisation.mp4	20.8 MB
4. Mean normalisation.srt	5.0 KB
4. Mean normalisation.vtt	4.5 KB
5. Mean normalisation Demo.mp4	47.3 MB
5. Mean normalisation Demo.srt	6.3 KB
5. Mean normalisation Demo.vtt	5.6 KB
6. Scaling to minimum and maximum values.mp4	17.9 MB
6. Scaling to minimum and maximum values.srt	3.9 KB
6. Scaling to minimum and maximum values.vtt	3.5 KB
7. MinMaxScaling Demo.mp4	27.1 MB
7. MinMaxScaling Demo.srt	3.6 KB
7. MinMaxScaling Demo.vtt	3.2 KB
8. Maximum absolute scaling.mp4	15.3 MB
8. Maximum absolute scaling.srt	3.4 KB
8. Maximum absolute scaling.vtt	3.0 KB
9. MaxAbsScaling Demo.mp4	33.0 MB
9. MaxAbsScaling Demo.srt	4.7 KB
9. MaxAbsScaling Demo.vtt	4.1 KB
/11. Engineering mixed variables/
1. Engineering mixed variables.mp4	16.0 MB
1. Engineering mixed variables.srt	4.1 KB
1. Engineering mixed variables.vtt	3.6 KB
2. Engineering mixed variables Demo.mp4	47.7 MB
2. Engineering mixed variables Demo.srt	7.4 KB
2. Engineering mixed variables Demo.vtt	6.6 KB
/12. Engineering datetime variables/
1. Engineering datetime variables.mp4	24.3 MB
1. Engineering datetime variables.srt	5.6 KB
1. Engineering datetime variables.vtt	5.0 KB
2. Engineering dates Demo.mp4	56.6 MB
2. Engineering dates Demo.srt	9.3 KB
2. Engineering dates Demo.vtt	8.2 KB
3. Engineering time variables and different timezones.mp4	35.1 MB
3. Engineering time variables and different timezones.srt	5.5 KB
3. Engineering time variables and different timezones.vtt	4.8 KB
/13. Assembling a feature engineering pipeline/
1. Classification pipeline.mp4	142.6 MB
1. Classification pipeline.srt	15.9 KB
1. Classification pipeline.vtt	14.2 KB
2. Regression pipeline.mp4	165.2 MB
2. Regression pipeline.srt	17.2 KB
2. Regression pipeline.vtt	15.2 KB
3. Beat the performance by engineering features.html	0.2 KB
/14. Final section Next steps/
1. BONUS Discounts on my other courses!.html	1.0 KB
/2. Variable Types/
1. Variables Intro.mp4	16.0 MB
1. Variables Intro.srt	3.6 KB
1. Variables Intro.vtt	3.2 KB
2. Numerical variables.mp4	28.2 MB
2. Numerical variables.srt	6.9 KB
2. Numerical variables.vtt	6.1 KB
3. Categorical variables.mp4	19.3 MB
3. Categorical variables.srt	4.7 KB
3. Categorical variables.vtt	4.2 KB
4. Date and time variables.mp4	10.3 MB
4. Date and time variables.srt	2.5 KB
4. Date and time variables.vtt	2.2 KB
5. Mixed variables.mp4	11.8 MB
5. Mixed variables.srt	3.0 KB
5. Mixed variables.vtt	2.7 KB
5.1 sample_s2.csv.csv	10.4 MB
6. Bonus More about the Lending Club dataset.html	0.8 KB
7. Quiz about variable types.html	0.2 KB
/3. Variable Characteristics/
1. Variable characteristics.mp4	21.9 MB
1. Variable characteristics.srt	3.7 KB
1. Variable characteristics.vtt	3.3 KB
10. Bonus Additional reading resources.html	4.8 KB
11. FAQ How can I learn more about machine learning.html	0.8 KB
2. Missing data.mp4	42.1 MB
2. Missing data.srt	9.2 KB
2. Missing data.vtt	8.1 KB
3. Cardinality - categorical variables.mp4	32.5 MB
3. Cardinality - categorical variables.srt	6.5 KB
3. Cardinality - categorical variables.vtt	5.8 KB
4. Rare Labels - categorical variables.mp4	35.5 MB
4. Rare Labels - categorical variables.srt	6.2 KB
4. Rare Labels - categorical variables.vtt	5.5 KB
5. Linear models assumptions.mp4	72.2 MB
5. Linear models assumptions.srt	11.8 KB
5. Linear models assumptions.vtt	10.5 KB
6. Variable distribution.mp4	34.4 MB
6. Variable distribution.srt	6.6 KB
6. Variable distribution.vtt	5.9 KB
7. Outliers.mp4	50.7 MB
7. Outliers.srt	10.7 KB
7. Outliers.vtt	9.5 KB
8. Variable magnitude.mp4	20.9 MB
8. Variable magnitude.srt	4.0 KB
8. Variable magnitude.vtt	3.5 KB
9. Bonus Machine learning algorithms overview.html	0.1 KB
9.1 ML_Comparison.pdf.pdf	304.8 KB
/4. Missing Data Imputation/
1. Introduction to missing data imputation.mp4	30.8 MB
1. Introduction to missing data imputation.srt	5.4 KB
1. Introduction to missing data imputation.vtt	4.8 KB
10. Mean or median imputation with Scikit-learn.mp4	92.4 MB
10. Mean or median imputation with Scikit-learn.srt	13.2 KB
10. Mean or median imputation with Scikit-learn.vtt	11.6 KB
11. Arbitrary value imputation with Scikit-learn.mp4	54.7 MB
11. Arbitrary value imputation with Scikit-learn.srt	6.7 KB
11. Arbitrary value imputation with Scikit-learn.vtt	5.9 KB
12. Frequent category imputation with Scikit-learn.mp4	35.8 MB
12. Frequent category imputation with Scikit-learn.srt	4.2 KB
12. Frequent category imputation with Scikit-learn.vtt	3.8 KB
13. Missing category imputation with Scikit-learn.mp4	25.8 MB
13. Missing category imputation with Scikit-learn.srt	3.1 KB
13. Missing category imputation with Scikit-learn.vtt	2.8 KB
14. Adding a missing indicator with Scikit-learn.mp4	37.4 MB
14. Adding a missing indicator with Scikit-learn.srt	4.8 KB
14. Adding a missing indicator with Scikit-learn.vtt	4.2 KB
15. Automatic determination of imputation method with Sklearn.mp4	84.3 MB
15. Automatic determination of imputation method with Sklearn.srt	9.2 KB
15. Automatic determination of imputation method with Sklearn.vtt	8.2 KB
16. Introduction to Feature-engine.mp4	42.4 MB
16. Introduction to Feature-engine.srt	6.6 KB
16. Introduction to Feature-engine.vtt	5.8 KB
17. Mean or median imputation with Feature-engine.mp4	40.5 MB
17. Mean or median imputation with Feature-engine.srt	5.2 KB
17. Mean or median imputation with Feature-engine.vtt	4.6 KB
18. Arbitrary value imputation with Feature-engine.mp4	28.0 MB
18. Arbitrary value imputation with Feature-engine.srt	3.3 KB
18. Arbitrary value imputation with Feature-engine.vtt	3.0 KB
19. End of distribution imputation with Feature-engine.mp4	40.8 MB
19. End of distribution imputation with Feature-engine.srt	5.4 KB
19. End of distribution imputation with Feature-engine.vtt	4.8 KB
2. Complete Case Analysis.mp4	48.9 MB
2. Complete Case Analysis.srt	8.7 KB
2. Complete Case Analysis.vtt	7.7 KB
20. Frequent category imputation with Feature-engine.mp4	16.9 MB
20. Frequent category imputation with Feature-engine.srt	2.1 KB
20. Frequent category imputation with Feature-engine.vtt	1.9 KB
21. Missing category imputation with Feature-engine.mp4	21.4 MB
21. Missing category imputation with Feature-engine.srt	2.6 KB
21. Missing category imputation with Feature-engine.vtt	2.3 KB
22. Random sample imputation with Feature-engine.mp4	16.9 MB
22. Random sample imputation with Feature-engine.srt	2.4 KB
22. Random sample imputation with Feature-engine.vtt	2.1 KB
23. Adding a missing indicator with Feature-engine.mp4	27.2 MB
23. Adding a missing indicator with Feature-engine.srt	4.0 KB
23. Adding a missing indicator with Feature-engine.vtt	3.6 KB
24. Overview of missing value imputation methods.html	0.1 KB
24.1 NA_methods_Comparison.pdf.pdf	280.4 KB
25. Conclusion when to use each missing data imputation method.html	2.7 KB
3. Mean or median imputation.mp4	54.7 MB
3. Mean or median imputation.srt	10.5 KB
3. Mean or median imputation.vtt	9.3 KB
4. Arbitrary value imputation.mp4	42.0 MB
4. Arbitrary value imputation.srt	8.6 KB
4. Arbitrary value imputation.vtt	7.7 KB
5. End of distribution imputation.mp4	29.5 MB
5. End of distribution imputation.srt	6.2 KB
5. End of distribution imputation.vtt	5.5 KB
6. Frequent category imputation.mp4	52.2 MB
6. Frequent category imputation.srt	8.4 KB
6. Frequent category imputation.vtt	7.5 KB
7. Missing category imputation.mp4	29.5 MB
7. Missing category imputation.srt	4.9 KB
7. Missing category imputation.vtt	4.4 KB
8. Random sample imputation.mp4	107.6 MB
8. Random sample imputation.srt	18.1 KB
8. Random sample imputation.vtt	16.0 KB
9. Adding a missing indicator.mp4	32.6 MB
9. Adding a missing indicator.srt	6.7 KB
9. Adding a missing indicator.vtt	6.0 KB
/5. Multivariate Missing Data Imputation/
1. Multivariate Imputation - COMING IN 2020.html	0.1 KB
/6. Categorical Variable Encoding/
1. Categorical encoding Introduction.mp4	35.7 MB
1. Categorical encoding Introduction.srt	8.1 KB
1. Categorical encoding Introduction.vtt	7.3 KB
10. Target guided ordinal encoding.mp4	13.5 MB
10. Target guided ordinal encoding.srt	3.5 KB
10. Target guided ordinal encoding.vtt	3.1 KB
11. Target guided ordinal encoding Demo.mp4	72.1 MB
11. Target guided ordinal encoding Demo.srt	9.6 KB
11. Target guided ordinal encoding Demo.vtt	8.6 KB
12. Mean encoding.mp4	13.5 MB
12. Mean encoding.srt	3.0 KB
12. Mean encoding.vtt	2.7 KB
13. Mean encoding Demo.mp4	44.1 MB
13. Mean encoding Demo.srt	6.5 KB
13. Mean encoding Demo.vtt	5.8 KB
14. Probability ratio encoding.mp4	47.9 MB
14. Probability ratio encoding.srt	7.3 KB
14. Probability ratio encoding.vtt	6.5 KB
15. Weight of evidence (WoE).mp4	21.6 MB
15. Weight of evidence (WoE).srt	5.2 KB
15. Weight of evidence (WoE).vtt	4.6 KB
16. Weight of Evidence Demo.mp4	47.3 MB
16. Weight of Evidence Demo.srt	8.2 KB
16. Weight of Evidence Demo.vtt	7.3 KB
17. Comparison of categorical variable encoding.mp4	82.3 MB
17. Comparison of categorical variable encoding.srt	12.7 KB
17. Comparison of categorical variable encoding.vtt	11.2 KB
18. Rare label encoding.mp4	24.4 MB
18. Rare label encoding.srt	5.3 KB
18. Rare label encoding.vtt	4.7 KB
19. Rare label encoding Demo.mp4	72.8 MB
19. Rare label encoding Demo.srt	12.3 KB
19. Rare label encoding Demo.vtt	10.9 KB
2. One hot encoding.mp4	33.3 MB
2. One hot encoding.srt	7.1 KB
2. One hot encoding.vtt	6.3 KB
20. Binary encoding and feature hashing.mp4	32.4 MB
20. Binary encoding and feature hashing.srt	7.7 KB
20. Binary encoding and feature hashing.vtt	6.8 KB
21. Bonus Additional reading resources.html	2.5 KB
3. One-hot-encoding Demo.mp4	95.8 MB
3. One-hot-encoding Demo.srt	18.0 KB
3. One-hot-encoding Demo.vtt	15.8 KB
4. One hot encoding of top categories.mp4	19.0 MB
4. One hot encoding of top categories.srt	3.4 KB
4. One hot encoding of top categories.vtt	3.0 KB
5. One hot encoding of top categories Demo.mp4	60.0 MB
5. One hot encoding of top categories Demo.srt	9.9 KB
5. One hot encoding of top categories Demo.vtt	8.8 KB
6. Ordinal encoding Label encoding.mp4	9.9 MB
6. Ordinal encoding Label encoding.srt	2.1 KB
6. Ordinal encoding Label encoding.vtt	1.9 KB
7. Ordinal encoding Demo.mp4	60.3 MB
7. Ordinal encoding Demo.srt	9.6 KB
7. Ordinal encoding Demo.vtt	8.5 KB
8. Count or frequency encoding.mp4	16.5 MB
8. Count or frequency encoding.srt	3.8 KB
8. Count or frequency encoding.vtt	3.4 KB
9. Count encoding Demo.mp4	34.1 MB
9. Count encoding Demo.srt	5.0 KB
9. Count encoding Demo.vtt	4.5 KB
/7. Variable Transformation/
1. Variable Transformation Introduction.mp4	19.6 MB
1. Variable Transformation Introduction.srt	5.6 KB
1. Variable Transformation Introduction.vtt	5.0 KB
2. Variable Transformation with Numpy and SciPy.mp4	51.8 MB
2. Variable Transformation with Numpy and SciPy.srt	8.7 KB
2. Variable Transformation with Numpy and SciPy.vtt	7.7 KB
3. variable Transformation with Scikit-learn.mp4	49.4 MB
3. variable Transformation with Scikit-learn.srt	7.8 KB
3. variable Transformation with Scikit-learn.vtt	7.0 KB
4. Variable transformation with Feature-engine.mp4	24.8 MB
4. Variable transformation with Feature-engine.srt	4.1 KB
4. Variable transformation with Feature-engine.vtt	3.7 KB
/8. Discretisation/
1. Discretisation Introduction.mp4	16.2 MB
1. Discretisation Introduction.srt	3.5 KB
1. Discretisation Introduction.vtt	3.1 KB
10. Discretisation with classification trees.mp4	27.9 MB
10. Discretisation with classification trees.srt	5.6 KB
10. Discretisation with classification trees.vtt	5.0 KB
11. Discretisation with decision trees using Scikit-learn.mp4	84.1 MB
11. Discretisation with decision trees using Scikit-learn.srt	13.4 KB
11. Discretisation with decision trees using Scikit-learn.vtt	11.9 KB
12. Discretisation with decision trees using Feature-engine.mp4	29.8 MB
12. Discretisation with decision trees using Feature-engine.srt	4.0 KB
12. Discretisation with decision trees using Feature-engine.vtt	3.6 KB
13. Domain knowledge discretisation.mp4	26.9 MB
13. Domain knowledge discretisation.srt	4.2 KB
13. Domain knowledge discretisation.vtt	3.7 KB
14. Bonus Additional reading resources.html	1.4 KB
14.1 15.5_Bonus_Additional_reading_resources.zip.zip	1.1 KB
2. Equal-width discretisation.mp4	22.6 MB
2. Equal-width discretisation.srt	4.5 KB
2. Equal-width discretisation.vtt	4.0 KB
3. Equal-width discretisation Demo.mp4	82.9 MB
3. Equal-width discretisation Demo.srt	12.8 KB
3. Equal-width discretisation Demo.vtt	11.3 KB
4. Equal-frequency discretisation.mp4	23.6 MB
4. Equal-frequency discretisation.srt	4.8 KB
4. Equal-frequency discretisation.vtt	4.3 KB
5. Equal-frequency discretisation Demo.mp4	49.6 MB
5. Equal-frequency discretisation Demo.srt	7.9 KB
5. Equal-frequency discretisation Demo.vtt	7.0 KB
6. K-means discretisation.mp4	19.8 MB
6. K-means discretisation.srt	4.8 KB
6. K-means discretisation.vtt	4.3 KB
7. K-means discretisation Demo.mp4	19.7 MB
7. K-means discretisation Demo.srt	3.3 KB
7. K-means discretisation Demo.vtt	2.9 KB
8. Discretisation plus categorical encoding.mp4	14.0 MB
8. Discretisation plus categorical encoding.srt	2.8 KB
8. Discretisation plus categorical encoding.vtt	2.5 KB
9. Discretisation plus encoding Demo.mp4	38.0 MB
9. Discretisation plus encoding Demo.srt	6.7 KB
9. Discretisation plus encoding Demo.vtt	5.9 KB
/9. Outlier Handling/
1. Outlier Engineering Intro.mp4	44.0 MB
1. Outlier Engineering Intro.srt	7.9 KB
1. Outlier Engineering Intro.vtt	7.0 KB
2. Outlier trimming.mp4	53.6 MB
2. Outlier trimming.srt	8.5 KB
2. Outlier trimming.vtt	7.6 KB
3. Outlier capping with IQR.mp4	45.7 MB
3. Outlier capping with IQR.srt	6.9 KB
3. Outlier capping with IQR.vtt	6.2 KB
4. Outlier capping with mean and std.mp4	36.3 MB
4. Outlier capping with mean and std.srt	4.9 KB
4. Outlier capping with mean and std.vtt	4.4 KB
5. Outlier capping with quantiles.mp4	25.6 MB
5. Outlier capping with quantiles.srt	3.6 KB
5. Outlier capping with quantiles.vtt	3.3 KB
6. Arbitrary capping.mp4	20.6 MB
6. Arbitrary capping.srt	3.9 KB
6. Arbitrary capping.vtt	3.5 KB
7. Additional reading resources.html	0.4 KB
Total files 338