What you’ll learn
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Learn multiple techniques for missing data imputation
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Transform categorical variables into numbers while capturing meaningful information
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Learn how to deal with infrequent, rare and unseen categories
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Transform skewed variables into Gaussian
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Convert numerical variables into discrete
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Remove outliers from your variables
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Extract meaningful features from dates and time variables
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Learn techniques used in organisations worldwide and in data competitions
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Increase your repertoire of techniques to preprocess data and build more powerful machine learning models
Specifically, you will learn:
- How to impute your missing data
- How to encode your categorical variables
- How to transform your numerical variables so they meet ML model assumptions
- How to convert your numerical variables into discrete intervals
- How to remove outliers
- How to handle date and time variables
- How to work with different time zones
- How to handle mixed variables which contain strings and numbers
Who this course is for:
- Data Scientists who want to get started in pre-processing datasets to build machine learning models
- Data Scientists who want to learn more techniques for feature engineering for machine learning
- Data Scientist who want to improve their coding skills and best programming practices for feature engineering
- Software engineers, mathematicians and academics switching careers into data science
- Data Scientists who want to try different feature engineering techniques on data competitions
- Software engineers who want to learn how to use Scikit-learn and other open-source packages for feature engineering
Deal Score0
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