[CourseClub.NET] Coursera - How to Win a Data Science Competition Learn from Top Kagglers torrent, magnet, bt

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[CourseClub.NET] Coursera - How to Win a Data Science Competition Learn from Top Kagglers

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2018-10-08

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CourseClub NET Coursera How to Win Data Science Competition Learn from Top Kagglers

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File Name

Size

001.Welcome to How to win a data science competition/001. Introduction.mp4

9.72MB

001.Welcome to How to win a data science competition/001. Introduction.srt

2.72KB

001.Welcome to How to win a data science competition/002. Meet your lecturers.mp4

13.85MB

001.Welcome to How to win a data science competition/002. Meet your lecturers.srt

3.56KB

001.Welcome to How to win a data science competition/003. Course overview.mp4

34.63MB

001.Welcome to How to win a data science competition/003. Course overview.srt

10.19KB

002.Competition mechanics/004. Competition Mechanics.mp4

24.94MB

002.Competition mechanics/004. Competition Mechanics.srt

10.94KB

002.Competition mechanics/005. Kaggle Overview [screencast].mp4

32.38MB

002.Competition mechanics/005. Kaggle Overview [screencast].srt

9.17KB

002.Competition mechanics/006. Real World Application vs Competitions.mp4

20MB

002.Competition mechanics/006. Real World Application vs Competitions.srt

8.69KB

003.Recap of main ML algorithms/007. Recap of main ML algorithms.mp4

33.45MB

003.Recap of main ML algorithms/007. Recap of main ML algorithms.srt

13.59KB

004.Software Hardware requirements/008. Software Hardware Requirements.mp4

21.53MB

004.Software Hardware requirements/008. Software Hardware Requirements.srt

7.92KB

005.Feature preprocessing and generation with respect to models/009. Overview.mp4

25.67MB

005.Feature preprocessing and generation with respect to models/009. Overview.srt

8.95KB

005.Feature preprocessing and generation with respect to models/010. Numeric features.mp4

48.31MB

005.Feature preprocessing and generation with respect to models/010. Numeric features.srt

18.56KB

005.Feature preprocessing and generation with respect to models/011. Categorical and ordinal features.mp4

40.55MB

005.Feature preprocessing and generation with respect to models/011. Categorical and ordinal features.srt

13.23KB

005.Feature preprocessing and generation with respect to models/012. Datetime and coordinates.mp4

32.41MB

005.Feature preprocessing and generation with respect to models/012. Datetime and coordinates.srt

10.22KB

005.Feature preprocessing and generation with respect to models/013. Handling missing values.mp4

37.85MB

005.Feature preprocessing and generation with respect to models/013. Handling missing values.srt

12.77KB

006.Feature extraction from text and images/014. Bag of words.mp4

38.04MB

006.Feature extraction from text and images/014. Bag of words.srt

13.66KB

006.Feature extraction from text and images/015. Word2vec, CNN.mp4

45.96MB

006.Feature extraction from text and images/015. Word2vec, CNN.srt

16.84KB

007.Final project/016. Final project overview.mp4

17.81MB

007.Final project/016. Final project overview.srt

5.43KB

008.Exploratory data analysis/017. Exploratory data analysis.mp4

23.95MB

008.Exploratory data analysis/017. Exploratory data analysis.srt

9.69KB

008.Exploratory data analysis/018. Building intuition about the data.mp4

22.29MB

008.Exploratory data analysis/018. Building intuition about the data.srt

9.43KB

008.Exploratory data analysis/019. Exploring anonymized data.mp4

43.05MB

008.Exploratory data analysis/019. Exploring anonymized data.srt

18.21KB

008.Exploratory data analysis/020. Visualizations.mp4

42.59MB

008.Exploratory data analysis/020. Visualizations.srt

16.13KB

008.Exploratory data analysis/021. Dataset cleaning and other things to check.mp4

25.83MB

008.Exploratory data analysis/021. Dataset cleaning and other things to check.srt

9.57KB

009.EDA examples/022. Springleaf competition EDA I.mp4

20.11MB

009.EDA examples/022. Springleaf competition EDA I.srt

8.96KB

009.EDA examples/023. Springleaf competition EDA II.mp4

44.36MB

009.EDA examples/023. Springleaf competition EDA II.srt

19.87KB

009.EDA examples/024. Numerai competition EDA.mp4

21.95MB

009.EDA examples/024. Numerai competition EDA.srt

7.72KB

010.Validation/025. Validation and overfitting.mp4

34.09MB

010.Validation/025. Validation and overfitting.srt

13.29KB

010.Validation/026. Validation strategies.mp4

26.15MB

010.Validation/026. Validation strategies.srt

9.08KB

010.Validation/027. Data splitting strategies.mp4

56.16MB

010.Validation/027. Data splitting strategies.srt

18.69KB

010.Validation/028. Problems occurring during validation.mp4

71.05MB

010.Validation/028. Problems occurring during validation.srt

25.44KB

011.Data leakages/029. Basic data leaks.mp4

22.14MB

011.Data leakages/029. Basic data leaks.srt

8.1KB

011.Data leakages/030. Leaderboard probing and examples of rare data leaks.mp4

34.07MB

011.Data leakages/030. Leaderboard probing and examples of rare data leaks.srt

12.22KB

011.Data leakages/031. Expedia challenge.mp4

35.65MB

011.Data leakages/031. Expedia challenge.srt

11.39KB

012.Metrics optimization/032. Motivation.mp4

27.49MB

012.Metrics optimization/032. Motivation.srt

10.58KB

012.Metrics optimization/033. Regression metrics review I.mp4

46.38MB

012.Metrics optimization/033. Regression metrics review I.srt

17.48KB

012.Metrics optimization/034. Regression metrics review II.mp4

29.2MB

012.Metrics optimization/034. Regression metrics review II.srt

9.53KB

012.Metrics optimization/035. Classification metrics review.mp4

70.29MB

012.Metrics optimization/035. Classification metrics review.srt

24.27KB

012.Metrics optimization/036. General approaches for metrics optimization.mp4

23.71MB

012.Metrics optimization/036. General approaches for metrics optimization.srt

8.01KB

012.Metrics optimization/037. Regression metrics optimization.mp4

35.84MB

012.Metrics optimization/037. Regression metrics optimization.srt

12.08KB

012.Metrics optimization/038. Classification metrics optimization I.mp4

26.25MB

012.Metrics optimization/038. Classification metrics optimization I.srt

8.95KB

012.Metrics optimization/039. Classification metrics optimization II.mp4

25.24MB

012.Metrics optimization/039. Classification metrics optimization II.srt

8.71KB

013.Mean encodings/040. Concept of mean encoding.mp4

30.55MB

013.Mean encodings/040. Concept of mean encoding.srt

9.9KB

013.Mean encodings/041. Regularization.mp4

28.37MB

013.Mean encodings/041. Regularization.srt

9.16KB

013.Mean encodings/042. Extensions and generalizations.mp4

39.24MB

013.Mean encodings/042. Extensions and generalizations.srt

12.21KB

014.Hyperparameter tuning/043. Hyperparameter tuning I.mp4

24.96MB

014.Hyperparameter tuning/043. Hyperparameter tuning I.srt

8.84KB

014.Hyperparameter tuning/044. Hyperparameter tuning II.mp4

43.3MB

014.Hyperparameter tuning/044. Hyperparameter tuning II.srt

15.13KB

014.Hyperparameter tuning/045. Hyperparameter tuning III.mp4

47.18MB

014.Hyperparameter tuning/045. Hyperparameter tuning III.srt

15.17KB

015.Tips and tricks/046. Practical guide.mp4

59.12MB

015.Tips and tricks/046. Practical guide.srt

22.21KB

015.Tips and tricks/047. KazAnova's competition pipeline, part 1.mp4

33.81MB

015.Tips and tricks/047. KazAnova's competition pipeline, part 1.srt

23.44KB

015.Tips and tricks/048. KazAnova's competition pipeline, part 2.mp4

32MB

015.Tips and tricks/048. KazAnova's competition pipeline, part 2.srt

21.55KB

016.Advanced features II/049. Statistics and distance based features.mp4

20.96MB

016.Advanced features II/049. Statistics and distance based features.srt

6.82KB

016.Advanced features II/050. Matrix factorizations.mp4

24.14MB

016.Advanced features II/050. Matrix factorizations.srt

9.02KB

016.Advanced features II/051. Feature Interactions.mp4

20.41MB

016.Advanced features II/051. Feature Interactions.srt

7.77KB

016.Advanced features II/052. t-SNE.mp4

21.56MB

016.Advanced features II/052. t-SNE.srt

7.47KB

017.Ensembling/053. Introduction into ensemble methods.mp4

10.68MB

017.Ensembling/053. Introduction into ensemble methods.srt

7.01KB

017.Ensembling/054. Bagging.mp4

15.93MB

017.Ensembling/054. Bagging.srt

11KB

017.Ensembling/055. Boosting.mp4

27.94MB

017.Ensembling/055. Boosting.srt

19.17KB

017.Ensembling/056. Stacking.mp4

30.78MB

017.Ensembling/056. Stacking.srt

18.99KB

017.Ensembling/057. StackNet.mp4

29.25MB

017.Ensembling/057. StackNet.srt

18.07KB

017.Ensembling/058. Ensembling Tips and Tricks.mp4

25.59MB

017.Ensembling/058. Ensembling Tips and Tricks.srt

17.97KB

018.Competitions go through/059. Crowdflower Competition.mp4

36.12MB

018.Competitions go through/059. Crowdflower Competition.srt

15.47KB

018.Competitions go through/060. Springleaf Marketing Response.mp4

24.24MB

018.Competitions go through/060. Springleaf Marketing Response.srt

7.9KB

018.Competitions go through/061. Microsoft Malware Classification Challenge.mp4

68.35MB

018.Competitions go through/061. Microsoft Malware Classification Challenge.srt

22.98KB

018.Competitions go through/062. Walmart Trip Type Classification.mp4

29.55MB

018.Competitions go through/062. Walmart Trip Type Classification.srt

9.98KB

018.Competitions go through/063. Acquire Valued Shoppers Challenge, part 1.mp4

34.77MB

018.Competitions go through/063. Acquire Valued Shoppers Challenge, part 1.srt

25.14KB

018.Competitions go through/064. Acquire Valued Shoppers Challenge, part 2.mp4

30.87MB

018.Competitions go through/064. Acquire Valued Shoppers Challenge, part 2.srt

21.89KB

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