Feature Engineering for Machine Learning
What you’ll learn
- Learn multiple techniques for missing data imputation
- Transform categorical variables into numbers while capturing meaningful information
- Learn how to deal with infrequent, rare and unseen categories
- Transform skewed variables into Gaussian
- Convert numerical variables into discrete
- Remove outliers from your variables
- Extract meaningful features from dates and time variables
- Learn techniques used in organisations worldwide and in data competitions
- Increase your repertoire of techniques to preprocess data and build more powerful machine learning models
- A Python installation
- Jupyter notebook installation
- Python coding skills
- Some experience with Numpy and Pandas
- Familiarity with Machine Learning algorithms
- Familiarity with Scikit-Learn
NEW! Updated in November 2020 for the latest software versions, including use of new tools and open-source packages, and additional feature engineering techniques.
Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online . In this course, you will learn how to engineer features and build more powerful machine learning models.
Who is this course for?
So, you’ve made your first steps into data science, you know the most commonly used prediction models, you perhaps even built a linear regression or a classification tree model. At this stage you’re probably starting to encounter some challenges - you realize that your data set is dirty, there are lots of values missing, some variables contain labels instead of numbers, others do not meet the assumptions of the models, and on top of everything you wonder whether this is the right way to code things up. And to make things more complicated, you can’t find many consolidated resources about feature engineering. Maybe even just blogs? So you may start to wonder: how are things really done in tech companies?
This course will help you! This is the most comprehensive online course in variable engineering . You will learn a huge variety of engineering techniques used worldwide in different organizations and in data science competitions, to clean and transform your data and variables.
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