This course, Discover Feature Engineering for Machine Learning, focuses on the crucial aspect of feature engineering in machine learning models. Feature engineering plays a pivotal role in determining the performance of a model, as it involves incorporating domain knowledge into the process. By understanding the principles of sound feature engineering, you can effectively reduce the number of variables, enhance the speed of learning algorithms, improve interpretability, and prevent overfitting. In this course, you will gain proficiency in implementing feature engineering techniques using the R tidymodels framework. The emphasis will be on utilizing the recipe package, which enables you to create, extract, transform, and select the most suitable features for your model. By the end of this course, you will possess the skills to engineer features and construct superior machine learning models. When confronted with a new dataset, you will be able to identify and choose relevant features while disregarding non-informative ones. This ability will enable your model to run faster without compromising accuracy. Additionally, you will feel confident in applying transformations and generating new features to enhance the efficiency, interpretability, and accuracy of your models.