[Coursera] How to Win a Data Science Competition: Learn from Top Kagglers

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size2 GB
  • Uploaded Bycoursezone
  • Downloads65
  • Last checkedAug. 30th '18
  • Date uploadedAug. 29th '18
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For more Course: https://coursezone.net



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.

Files:

[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. Introduction.srt (2.7 KB)
  • 002. Meet your lecturers.mp4 (13.8 MB)
  • 002. Meet your lecturers.srt (3.6 KB)
  • 003. Course overview.mp4 (34.6 MB)
  • 003. Course overview.srt (10.2 KB)
002.Competition mechanics
  • 004. Competition Mechanics.mp4 (24.9 MB)
  • 004. Competition Mechanics.srt (10.9 KB)
  • 005. Kaggle Overview [screencast].mp4 (32.4 MB)
  • 005. Kaggle Overview [screencast].srt (9.2 KB)
  • 006. Real World Application vs Competitions.mp4 (20.0 MB)
  • 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)
  • 007. Recap of main ML algorithms.srt (13.6 KB)
004.Software Hardware requirements
  • 008. Software Hardware Requirements.mp4 (21.5 MB)
  • 008. Software Hardware Requirements.srt (7.9 KB)
005.Feature preprocessing and generation with respect to models
  • 009. Overview.mp4 (25.7 MB)
  • 009. Overview.srt (9.0 KB)
  • 010. Numeric features.mp4 (48.3 MB)
  • 010. Numeric features.srt (18.6 KB)
  • 011. Categorical and ordinal features.mp4 (40.5 MB)
  • 011. Categorical and ordinal features.srt (13.2 KB)
  • 012. Datetime and coordinates.mp4 (32.4 MB)
  • 012. Datetime and coordinates.srt (10.2 KB)
  • 013. Handling missing values.mp4 (37.9 MB)
  • 013. Handling missing values.srt (12.8 KB)
006.Feature extraction from text and images
  • 014. Bag of words.mp4 (38.0 MB)
  • 014. Bag of words.srt (13.7 KB)
  • 015. Word2vec, CNN.mp4 (46.0 MB)
  • 015. Word2vec, CNN.srt (16.8 KB)
007.Final project
  • 016. Final project overview.mp4 (17.8 MB)
  • 016. Final project overview.srt (5.4 KB)
008.Exploratory data analysis
  • 017. Exploratory data analysis.mp4 (24.0 MB)
  • 017. Exploratory data analysis.srt (9.7 KB)
  • 018. Building intuition about the data.mp4 (22.3 MB)
  • 018. Building intuition about the data.srt (9.4 KB)
  • 019. Exploring anonymized data.mp4 (43.0 MB)
  • 019. Exploring anonymized data.srt (18.2 KB)
  • 020. Visualizations.mp4 (42.6 MB)
  • 020. Visualizations.srt (16.1 KB)
  • 021. Dataset cleaning and other things to check.mp4 (25.8 MB)
  • 021. Dataset cleaning and other things to check.srt (9.6 KB)
009.EDA examples
  • 022. Springleaf competition EDA I.mp4 (20.1 MB)
  • 022. Springleaf competition EDA I.srt (9.0 KB)
  • 023. Springleaf competition EDA II.mp4 (44.4 MB)
  • 023. Springleaf competition EDA II.srt (19.9 KB)
  • 024. Numerai competition EDA.mp4 (22.0 MB)
  • 024. Numerai competition EDA.srt (7.7 KB)
010.Validation
  • 025. Validation and overfitting.mp4 (34.1 MB)
  • 025. Validation and overfitting.srt (13.3 KB)
  • 026. Validation strategies.mp4 (26.1 MB)
  • 026. Validation strategies.srt (9.1 KB)
  • 027. Data splitting strategies.mp4 (56.2 MB)
  • 027. Data splitting strategies.srt (18.7 KB)
  • 028. Problems occurring during validation.mp4 (26.5 MB)
  • 028. Problems occurring during validation.srt (25.4 KB)
011.Data leakages
  • 029. Basic data leaks.mp4 (22.1 MB)
  • 029. Basic data leaks.srt (8.1 KB)
  • 030. Leaderboard probing and examples of rare data leaks.mp4 (34.1 MB)
  • 030. Leaderboard probing and examples of rare data leaks.srt (12.2 KB)
  • 031. Expedia challenge.mp4 (35.7 MB)
  • 031. Expedia challenge.srt (11.4 KB)
012.Metrics optimization
  • 032. Motivation.mp4 (27.5 MB)
  • 032. Motivation.srt (10.6 KB)
  • 033. Regression metrics review I.mp4 (46.4 MB)
  • 033. Regression metrics review I.srt (17.5 KB)
  • 034. Regression metrics review II.mp4 (29.2 MB)
  • 034. Regression metrics review II.srt (9.5 KB)
  • 035. Classification metrics review.mp4 (70.3 MB)
  • 035. Classification metrics review.srt (24.3 KB)
  • 036. General approaches for metrics optimization.mp4 (23.7 MB)
  • 036. General approaches for metrics optimization.srt (8.0 KB)
  • 037. Regression metrics optimization.mp4 (35.8 MB)
  • 037. Regression metrics optimization.srt (12.1 KB)
  • 038. Classification metrics optimization I.mp4 (26.3 MB)
  • 038. Classification metrics optimization I.srt (8.9 KB)
  • 039. Classification metrics optimization II.mp4 (25.2 MB)
  • 039. Classification metrics optimization II.srt (8.7 KB)
013.Mean encodings
  • 040. Concept of mean encoding.mp4 (30.5 MB)
  • 040. Concept of mean encoding.srt (9.9 KB)
  • 041. Regularization.mp4 (28.4 MB)
  • 041. Regularization.srt (9.2 KB)
  • 042. Extensions and generalizations.mp4 (39.2 MB)
  • 042. Extensions and generalizations.srt (12.2 KB)
014.Hyperparameter tuning
  • 043. Hyperparameter tuning I.mp4 (25.0 MB)
  • 043. Hyperparameter tuning I.srt (8.8 KB)
  • 044. Hyperparameter tuning II.mp4 (43.3 MB)
  • 044. Hyperparameter tuning II.srt (15.1 KB)
  • 045. Hyperparameter tuning III.mp4 (47.2 MB)
  • 045. Hyperparameter tuning III.srt (15.2 KB)
015.Tips and tricks
  • 046. Practical guide.mp4 (59.1 MB)
  • 046. Practical guide.srt (22.2 KB)
  • 047. KazAnova's competition pipeline, part 1.mp4 (33.8 MB)
  • 047. KazAnova's competition pipeline, part 1.srt (23.4 KB)
  • 048. KazAnova's competition pipeline, part 2.mp4 (32.0 MB)
  • 048. KazAnova's competition pipeline, part 2.srt (21.6 KB)
016.Advanced features II

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