Are you frequently faced with datasets that have an overwhelming number of features? Do you find it challenging to determine which features are truly important? If so, this course is designed to teach you dimensionality reduction techniques that can simplify your data and the models you build, while still retaining the essential information and achieving good predictive performance. Why is it important to learn dimensionality reduction? In today's information age, we are bombarded with an excess of data. Being able to extract the most relevant information from this data is a valuable skill. By reducing the dimensionality of your data, models can be trained more efficiently. In production, smaller models lead to faster response times. Additionally, smaller datasets and models are often easier to comprehend. Dimensionality reduction serves as your Occam's razor in the field of data science. What will you gain from this course? You will learn the distinction between feature selection and feature extraction. Using the R programming language, you will acquire the skills to identify and eliminate features that provide little or redundant information, while retaining those that are most informative. This process is known as feature selection. Furthermore, you will learn how to extract condensed components that combine multiple features and contain the maximum amount of information. This technique is referred to as feature extraction. Most importantly, this course will guide you in utilizing R's new tidymodel package to build models with fewer features, without compromising significant performance. Real-world data will be used throughout the course, ensuring practical application of the concepts learned.