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[FreeCoursesOnline.Me] Coursera - How To Win A Data Science Competition Learn From Top Kagglers

TORRENT SUMMARY

	Status:	All the torrents in this section have been verified by our verification system
Category:	Other
Infohash	6d41914c73aa9125507db10541d8c0680aaa1a73
Size:	2 GB in 99 files
Release Date(s):	Torrent Release: 25-10-2018 by user
Swarm:	0 Seeds & 0 Peers
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DESCRIPTION

[COURSERA] HOW TO WIN A DATA SCIENCE COMPETITION: LEARN FROM TOP KAGGLERS [FCO]

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.

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FILE LIST

Filename		Size
	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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