- Mar 27, 2022
[Download] Feature Engineering for Machine Learning For Free
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.
- Learn how to work with skewed variables.
- Convert numerical variables into discrete ones.
- Remove outliers from your variables.
- Extract useful features from dates and time variables.
- Learn techniques used in organizations 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.
Who this course is for:
- Data scientists who want to learn how to preprocess datasets in order to build machine learning models.
- Data scientists who want to learn more techniques for feature engineering for machine learning.
- Data scientists who want to improve their coding skills and programming practices for feature engineering.
- Software engineers, mathematicians and academics switching careers into data science.
- Data scientists interested in experimenting with various 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.