Dimensionality Reduction in R

Starting at USD 39.00
4 hours duration

Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.

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.

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DataCamp is an online learning platform that offers interactive courses and tutorials for data science and analytics. It provides a wide range of courses covering topics such as Python, R, SQL, machine learning, data visualization, and more. The platform offers a hands-on learning experience through coding exercises and projects, allowing users to practice and apply their skills in real-world scenarios. DataCamp also offers a personalized learning experience with adaptive learning technology that adjusts the course content based on the user's skill level and progress. It is widely used by individuals, professionals, and organizations to enhance their data science skills and stay up-to-date with the latest trends and technologies in the field.
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