[FreeCoursesOnline.Me] Coursera - How to Win a Data Science Competition Learn from Top Kagglers
File List
- 010.Validation/028. Problems occurring during validation.mp4 71.0 MB
- 012.Metrics optimization/035. Classification metrics review.mp4 70.3 MB
- 018.Competitions go through/061. Microsoft Malware Classification Challenge.mp4 68.4 MB
- 015.Tips and tricks/046. Practical guide.mp4 59.1 MB
- 010.Validation/027. Data splitting strategies.mp4 56.2 MB
- 005.Feature preprocessing and generation with respect to models/010. Numeric features.mp4 48.3 MB
- 014.Hyperparameter tuning/045. Hyperparameter tuning III.mp4 47.2 MB
- 012.Metrics optimization/033. Regression metrics review I.mp4 46.4 MB
- 006.Feature extraction from text and images/015. Word2vec, CNN.mp4 46.0 MB
- 009.EDA examples/023. Springleaf competition EDA II.mp4 44.4 MB
- 014.Hyperparameter tuning/044. Hyperparameter tuning II.mp4 43.3 MB
- 008.Exploratory data analysis/019. Exploring anonymized data.mp4 43.0 MB
- 008.Exploratory data analysis/020. Visualizations.mp4 42.6 MB
- 005.Feature preprocessing and generation with respect to models/011. Categorical and ordinal features.mp4 40.5 MB
- 013.Mean encodings/042. Extensions and generalizations.mp4 39.2 MB
- 006.Feature extraction from text and images/014. Bag of words.mp4 38.0 MB
- 005.Feature preprocessing and generation with respect to models/013. Handling missing values.mp4 37.9 MB
- 018.Competitions go through/059. Crowdflower Competition.mp4 36.1 MB
- 012.Metrics optimization/037. Regression metrics optimization.mp4 35.8 MB
- 011.Data leakages/031. Expedia challenge.mp4 35.7 MB
- 018.Competitions go through/063. Acquire Valued Shoppers Challenge, part 1.mp4 34.8 MB
- 001.Welcome to How to win a data science competition/003. Course overview.mp4 34.6 MB
- 010.Validation/025. Validation and overfitting.mp4 34.1 MB
- 011.Data leakages/030. Leaderboard probing and examples of rare data leaks.mp4 34.1 MB
- 015.Tips and tricks/047. KazAnova's competition pipeline, part 1.mp4 33.8 MB
- 003.Recap of main ML algorithms/007. Recap of main ML algorithms.mp4 33.4 MB
- 005.Feature preprocessing and generation with respect to models/012. Datetime and coordinates.mp4 32.4 MB
- 002.Competition mechanics/005. Kaggle Overview [screencast].mp4 32.4 MB
- 015.Tips and tricks/048. KazAnova's competition pipeline, part 2.mp4 32.0 MB
- 018.Competitions go through/064. Acquire Valued Shoppers Challenge, part 2.mp4 30.9 MB
- 017.Ensembling/056. Stacking.mp4 30.8 MB
- 013.Mean encodings/040. Concept of mean encoding.mp4 30.5 MB
- 018.Competitions go through/062. Walmart Trip Type Classification.mp4 29.5 MB
- 017.Ensembling/057. StackNet.mp4 29.2 MB
- 012.Metrics optimization/034. Regression metrics review II.mp4 29.2 MB
- 013.Mean encodings/041. Regularization.mp4 28.4 MB
- 017.Ensembling/055. Boosting.mp4 27.9 MB
- 012.Metrics optimization/032. Motivation.mp4 27.5 MB
- 012.Metrics optimization/038. Classification metrics optimization I.mp4 26.3 MB
- 010.Validation/026. Validation strategies.mp4 26.1 MB
- 008.Exploratory data analysis/021. Dataset cleaning and other things to check.mp4 25.8 MB
- 005.Feature preprocessing and generation with respect to models/009. Overview.mp4 25.7 MB
- 017.Ensembling/058. Ensembling Tips and Tricks.mp4 25.6 MB
- 012.Metrics optimization/039. Classification metrics optimization II.mp4 25.2 MB
- 014.Hyperparameter tuning/043. Hyperparameter tuning I.mp4 25.0 MB
- 002.Competition mechanics/004. Competition Mechanics.mp4 24.9 MB
- 018.Competitions go through/060. Springleaf Marketing Response.mp4 24.2 MB
- 016.Advanced features II/050. Matrix factorizations.mp4 24.1 MB
- 008.Exploratory data analysis/017. Exploratory data analysis.mp4 24.0 MB
- 012.Metrics optimization/036. General approaches for metrics optimization.mp4 23.7 MB
- 008.Exploratory data analysis/018. Building intuition about the data.mp4 22.3 MB
- 011.Data leakages/029. Basic data leaks.mp4 22.1 MB
- 009.EDA examples/024. Numerai competition EDA.mp4 22.0 MB
- 016.Advanced features II/052. t-SNE.mp4 21.6 MB
- 004.Software Hardware requirements/008. Software Hardware Requirements.mp4 21.5 MB
- 016.Advanced features II/049. Statistics and distance based features.mp4 21.0 MB
- 016.Advanced features II/051. Feature Interactions.mp4 20.4 MB
- 009.EDA examples/022. Springleaf competition EDA I.mp4 20.1 MB
- 002.Competition mechanics/006. Real World Application vs Competitions.mp4 20.0 MB
- 007.Final project/016. Final project overview.mp4 17.8 MB
- 017.Ensembling/054. Bagging.mp4 15.9 MB
- 001.Welcome to How to win a data science competition/002. Meet your lecturers.mp4 13.8 MB
- 017.Ensembling/053. Introduction into ensemble methods.mp4 10.7 MB
- 001.Welcome to How to win a data science competition/001. Introduction.mp4 9.7 MB
- 010.Validation/028. Problems occurring during validation.srt 25.4 KB
- 018.Competitions go through/063. Acquire Valued Shoppers Challenge, part 1.srt 25.1 KB
- 012.Metrics optimization/035. Classification metrics review.srt 24.3 KB
- 015.Tips and tricks/047. KazAnova's competition pipeline, part 1.srt 23.4 KB
- 018.Competitions go through/061. Microsoft Malware Classification Challenge.srt 23.0 KB
- 015.Tips and tricks/046. Practical guide.srt 22.2 KB
- 018.Competitions go through/064. Acquire Valued Shoppers Challenge, part 2.srt 21.9 KB
- 015.Tips and tricks/048. KazAnova's competition pipeline, part 2.srt 21.6 KB
- 009.EDA examples/023. Springleaf competition EDA II.srt 19.9 KB
- 017.Ensembling/055. Boosting.srt 19.2 KB
- 017.Ensembling/056. Stacking.srt 19.0 KB
- 010.Validation/027. Data splitting strategies.srt 18.7 KB
- 005.Feature preprocessing and generation with respect to models/010. Numeric features.srt 18.6 KB
- 008.Exploratory data analysis/019. Exploring anonymized data.srt 18.2 KB
- 017.Ensembling/057. StackNet.srt 18.1 KB
- 017.Ensembling/058. Ensembling Tips and Tricks.srt 18.0 KB
- 012.Metrics optimization/033. Regression metrics review I.srt 17.5 KB
- 006.Feature extraction from text and images/015. Word2vec, CNN.srt 16.8 KB
- 008.Exploratory data analysis/020. Visualizations.srt 16.1 KB
- 018.Competitions go through/059. Crowdflower Competition.srt 15.5 KB
- 014.Hyperparameter tuning/045. Hyperparameter tuning III.srt 15.2 KB
- 014.Hyperparameter tuning/044. Hyperparameter tuning II.srt 15.1 KB
- 006.Feature extraction from text and images/014. Bag of words.srt 13.7 KB
- 003.Recap of main ML algorithms/007. Recap of main ML algorithms.srt 13.6 KB
- 010.Validation/025. Validation and overfitting.srt 13.3 KB
- 005.Feature preprocessing and generation with respect to models/011. Categorical and ordinal features.srt 13.2 KB
- 005.Feature preprocessing and generation with respect to models/013. Handling missing values.srt 12.8 KB
- 011.Data leakages/030. Leaderboard probing and examples of rare data leaks.srt 12.2 KB
- 013.Mean encodings/042. Extensions and generalizations.srt 12.2 KB
- 012.Metrics optimization/037. Regression metrics optimization.srt 12.1 KB
- 011.Data leakages/031. Expedia challenge.srt 11.4 KB
- 017.Ensembling/054. Bagging.srt 11.0 KB
- 002.Competition mechanics/004. Competition Mechanics.srt 10.9 KB
- 012.Metrics optimization/032. Motivation.srt 10.6 KB
- 005.Feature preprocessing and generation with respect to models/012. Datetime and coordinates.srt 10.2 KB
- 001.Welcome to How to win a data science competition/003. Course overview.srt 10.2 KB
- 018.Competitions go through/062. Walmart Trip Type Classification.srt 10.0 KB
- 013.Mean encodings/040. Concept of mean encoding.srt 9.9 KB
- 008.Exploratory data analysis/017. Exploratory data analysis.srt 9.7 KB
- 008.Exploratory data analysis/021. Dataset cleaning and other things to check.srt 9.6 KB
- 012.Metrics optimization/034. Regression metrics review II.srt 9.5 KB
- 008.Exploratory data analysis/018. Building intuition about the data.srt 9.4 KB
- 002.Competition mechanics/005. Kaggle Overview [screencast].srt 9.2 KB
- 013.Mean encodings/041. Regularization.srt 9.2 KB
- 010.Validation/026. Validation strategies.srt 9.1 KB
- 016.Advanced features II/050. Matrix factorizations.srt 9.0 KB
- 009.EDA examples/022. Springleaf competition EDA I.srt 9.0 KB
- 005.Feature preprocessing and generation with respect to models/009. Overview.srt 9.0 KB
- 012.Metrics optimization/038. Classification metrics optimization I.srt 8.9 KB
- 014.Hyperparameter tuning/043. Hyperparameter tuning I.srt 8.8 KB
- 012.Metrics optimization/039. Classification metrics optimization II.srt 8.7 KB
- 002.Competition mechanics/006. Real World Application vs Competitions.srt 8.7 KB
- 011.Data leakages/029. Basic data leaks.srt 8.1 KB
- 012.Metrics optimization/036. General approaches for metrics optimization.srt 8.0 KB
- 004.Software Hardware requirements/008. Software Hardware Requirements.srt 7.9 KB
- 018.Competitions go through/060. Springleaf Marketing Response.srt 7.9 KB
- 016.Advanced features II/051. Feature Interactions.srt 7.8 KB
- 009.EDA examples/024. Numerai competition EDA.srt 7.7 KB
- 016.Advanced features II/052. t-SNE.srt 7.5 KB
- 017.Ensembling/053. Introduction into ensemble methods.srt 7.0 KB
- 016.Advanced features II/049. Statistics and distance based features.srt 6.8 KB
- 007.Final project/016. Final project overview.srt 5.4 KB
- 001.Welcome to How to win a data science competition/002. Meet your lecturers.srt 3.6 KB
- 001.Welcome to How to win a data science competition/001. Introduction.srt 2.7 KB
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