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

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

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Description
About this course: If you want to break into competitive data science, then this course is for you! Participating in predictive modelling competitions can help you gain practical experience, improve and harness your data modelling skills in various domains such as credit, insurance, marketing, natural language processing, sales’ forecasting and computer vision to name a few. At the same time you get to do it in a competitive context against thousands of participants where each one tries to build the most predictive algorithm. Pushing each other to the limit can result in better performance and smaller prediction errors. Being able to achieve high ranks consistently can help you accelerate your career in data science. In this course, you will learn to analyse and solve competitively such predictive modelling tasks. When you finish this class, you will: - Understand how to solve predictive modelling competitions efficiently and learn which of the skills obtained can be applicable to real-world tasks. - Learn how to preprocess the data and generate new features from various sources such as text and images. - Be taught advanced feature engineering techniques like generating mean-encodings, using aggregated statistical measures or finding nearest neighbors as a means to improve your predictions. - Be able to form reliable cross validation methodologies that help you benchmark your solutions and avoid overfitting or underfitting when tested with unobserved (test) data. - Gain experience of analysing and interpreting the data. You will become aware of inconsistencies, high noise levels, errors and other data-related issues such as leakages and you will learn how to overcome them. - Acquire knowledge of different algorithms and learn how to efficiently tune their hyperparameters and achieve top performance. - Master the art of combining different machine learning models and learn how to ensemble. - Get exposed to past (winning) solutions and codes and learn how to read them. Disclaimer : This is not a machine learning course in the general sense. This course will teach you how to get high-rank solutions against thousands of competitors with focus on practical usage of machine learning methods rather than the theoretical underpinnings behind them. Prerequisites: - Python: work with DataFrames in pandas, plot figures in matplotlib, import and train models from scikit-learn, XGBoost, LightGBM. - Machine Learning: basic understanding of linear models, K-NN, random forest, gradient boosting and neural networks.

File list
  • [Coursezone.net] Coursera - How to Win a Data Science Competition Learn from Top Kagglers
  • 001.Welcome to How to win a data science competition/001. Introduction.mp4 9.7 MB
  • 001.Welcome to How to win a data science competition/001. Introduction.srt 2.7 KB
  • 001.Welcome to How to win a data science competition/002. Meet your lecturers.mp4 13.8 MB
  • 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/003. Course overview.mp4 34.6 MB
  • 001.Welcome to How to win a data science competition/003. Course overview.srt 10.2 KB
  • 002.Competition mechanics/004. Competition Mechanics.mp4 24.9 MB
  • 002.Competition mechanics/004. Competition Mechanics.srt 10.9 KB
  • 002.Competition mechanics/005. Kaggle Overview [screencast].mp4 32.4 MB
  • 002.Competition mechanics/005. Kaggle Overview [screencast].srt 9.2 KB
  • 002.Competition mechanics/006. Real World Application vs Competitions.mp4 20 MB
  • 002.Competition mechanics/006. Real World Application vs Competitions.srt 8.7 KB
  • 003.Recap of main ML algorithms/007. Recap of main ML algorithms.mp4 33.4 MB
  • 003.Recap of main ML algorithms/007. Recap of main ML algorithms.srt 13.6 KB
  • 004.Software Hardware requirements/008. Software Hardware Requirements.mp4 21.5 MB
  • 004.Software Hardware requirements/008. Software Hardware Requirements.srt 7.9 KB
  • 005.Feature preprocessing and generation with respect to models/009. Overview.mp4 25.7 MB
  • 005.Feature preprocessing and generation with respect to models/009. Overview.srt 9 KB
  • 005.Feature preprocessing and generation with respect to models/010. Numeric features.mp4 48.3 MB
  • 005.Feature preprocessing and generation with respect to models/010. Numeric features.srt 18.6 KB
  • 005.Feature preprocessing and generation with respect to models/011. Categorical and ordinal features.mp4 40.5 MB
  • 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/012. Datetime and coordinates.mp4 32.4 MB
  • 005.Feature preprocessing and generation with respect to models/012. Datetime and coordinates.srt 10.2 KB
  • 005.Feature preprocessing and generation with respect to models/013. Handling missing values.mp4 37.9 MB
  • 005.Feature preprocessing and generation with respect to models/013. Handling missing values.srt 12.8 KB
  • 006.Feature extraction from text and images/014. Bag of words.mp4 38 MB
  • 006.Feature extraction from text and images/014. Bag of words.srt 13.7 KB
  • 006.Feature extraction from text and images/015. Word2vec, CNN.mp4 46 MB
  • 006.Feature extraction from text and images/015. Word2vec, CNN.srt 16.8 KB
  • 007.Final project/016. Final project overview.mp4 17.8 MB
  • 007.Final project/016. Final project overview.srt 5.4 KB
  • 008.Exploratory data analysis/017. Exploratory data analysis.mp4 24 MB
  • 008.Exploratory data analysis/017. Exploratory data analysis.srt 9.7 KB
  • 008.Exploratory data analysis/018. Building intuition about the data.mp4 22.3 MB
  • 008.Exploratory data analysis/018. Building intuition about the data.srt 9.4 KB
  • 008.Exploratory data analysis/019. Exploring anonymized data.mp4 43 MB
  • 008.Exploratory data analysis/019. Exploring anonymized data.srt 18.2 KB
  • 008.Exploratory data analysis/020. Visualizations.mp4 42.6 MB
  • 008.Exploratory data analysis/020. Visualizations.srt 16.1 KB
  • 008.Exploratory data analysis/021. Dataset cleaning and other things to check.mp4 25.8 MB
  • 008.Exploratory data analysis/021. Dataset cleaning and other things to check.srt 9.6 KB
  • 009.EDA examples/022. Springleaf competition EDA I.mp4 20.1 MB
  • 009.EDA examples/022. Springleaf competition EDA I.srt 9 KB
  • 009.EDA examples/023. Springleaf competition EDA II.mp4 44.4 MB
  • 009.EDA examples/023. Springleaf competition EDA II.srt 19.9 KB
  • 009.EDA examples/024. Numerai competition EDA.mp4 22 MB
  • 009.EDA examples/024. Numerai competition EDA.srt 7.7 KB
  • 010.Validation/025. Validation and overfitting.mp4 34.1 MB
  • 010.Validation/025. Validation and overfitting.srt 13.3 KB
  • 010.Validation/026. Validation strategies.mp4 26.1 MB
  • 010.Validation/026. Validation strategies.srt 9.1 KB
  • 010.Validation/027. Data splitting strategies.mp4 56.2 MB
  • 010.Validation/027. Data splitting strategies.srt 18.7 KB
  • 010.Validation/028. Problems occurring during validation.mp4 26.5 MB
  • 010.Validation/028. Problems occurring during validation.srt 25.4 KB
  • 011.Data leakages/029. Basic data leaks.mp4 22.1 MB
  • 011.Data leakages/029. Basic data leaks.srt 8.1 KB
  • 011.Data leakages/030. Leaderboard probing and examples of rare data leaks.mp4 34.1 MB
  • 011.Data leakages/030. Leaderboard probing and examples of rare data leaks.srt 12.2 KB
  • 011.Data leakages/031. Expedia challenge.mp4 35.7 MB
  • 011.Data leakages/031. Expedia challenge.srt 11.4 KB
  • 012.Metrics optimization/032. Motivation.mp4 27.5 MB
  • 012.Metrics optimization/032. Motivation.srt 10.6 KB
  • 012.Metrics optimization/033. Regression metrics review I.mp4 46.4 MB
  • 012.Metrics optimization/033. Regression metrics review I.srt 17.5 KB
  • 012.Metrics optimization/034. Regression metrics review II.mp4 29.2 MB
  • 012.Metrics optimization/034. Regression metrics review II.srt 9.5 KB
  • 012.Metrics optimization/035. Classification metrics review.mp4 70.3 MB
  • 012.Metrics optimization/035. Classification metrics review.srt 24.3 KB
  • 012.Metrics optimization/036. General approaches for metrics optimization.mp4 23.7 MB
  • 012.Metrics optimization/036. General approaches for metrics optimization.srt 8 KB
  • 012.Metrics optimization/037. Regression metrics optimization.mp4 35.8 MB
  • 012.Metrics optimization/037. Regression metrics optimization.srt 12.1 KB
  • 012.Metrics optimization/038. Classification metrics optimization I.mp4 26.3 MB
  • 012.Metrics optimization/038. Classification metrics optimization I.srt 8.9 KB
  • 012.Metrics optimization/039. Classification metrics optimization II.mp4 25.2 MB
  • 012.Metrics optimization/039. Classification metrics optimization II.srt 8.7 KB
  • 013.Mean encodings/040. Concept of mean encoding.mp4 30.5 MB
  • 013.Mean encodings/040. Concept of mean encoding.srt 9.9 KB
  • 013.Mean encodings/041. Regularization.mp4 28.4 MB
  • 013.Mean encodings/041. Regularization.srt 9.2 KB
  • 013.Mean encodings/042. Extensions and generalizations.mp4 39.2 MB
  • 013.Mean encodings/042. Extensions and generalizations.srt 12.2 KB
  • 014.Hyperparameter tuning/043. Hyperparameter tuning I.mp4 25 MB
  • 014.Hyperparameter tuning/043. Hyperparameter tuning I.srt 8.8 KB
  • 014.Hyperparameter tuning/044. Hyperparameter tuning II.mp4 43.3 MB
  • 014.Hyperparameter tuning/044. Hyperparameter tuning II.srt 15.1 KB
  • 014.Hyperparameter tuning/045. Hyperparameter tuning III.mp4 47.2 MB
  • 014.Hyperparameter tuning/045. Hyperparameter tuning III.srt 15.2 KB
  • 015.Tips and tricks/046. Practical guide.mp4 59.1 MB
  • 015.Tips and tricks/046. Practical guide.srt 22.2 KB
  • 015.Tips and tricks/047. KazAnova's competition pipeline, part 1.mp4 33.8 MB
  • 015.Tips and tricks/047. KazAnova's competition pipeline, part 1.srt 23.4 KB
  • 015.Tips and tricks/048. KazAnova's competition pipeline, part 2.mp4 32 MB
  • 015.Tips and tricks/048. KazAnova's competition pipeline, part 2.srt 21.6 KB
  • 016.Advanced features II/049. Statistics and distance based features.mp4 21 MB
  • 016.Advanced features II/049. Statistics and distance based features.srt 6.8 KB
  • 016.Advanced features II/050. Matrix factorizations.mp4 24.1 MB

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