Pluralsight - Feature Engineering and Dimensionality Reduction in R

Raw datasets are most often not very useful for training ML models. This course will teach you some important feature engineering techniques to improve the efficiency and accuracy of machine learning models.
Real datasets are messy, and building machine learning algorithms on raw data is often difficult. However, with a few important feature engineering techniques, we can improve the efficiency and accuracy of models. In this course, Feature Engineering and Dimensionality Reduction in R, you'll gain the ability to apply important feature engineering techniques on raw data before using them to train machine learning models. First, you'll explore how to handle missing values in a dataset. Next, you'll discover a few important data encoding and transformation techniques. Then, you'll learn linear and non-linear dimensionality reduction techniques to get a lower dimensional representation of the data. Finally, you'll learn how to remove superfluous features using recursive feature elimination. When you're finished with this course, you'll have the skills and knowledge needed to efficiently preprocess your dataset in a meaningful way, which can enhance the performance and efficiency of the underlying machine learning model.
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