Feature Engineering in R

Starting at USD 39.00
4 hours duration

Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.

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.

About Provider

DataCamp
DataCamp
Online Education Provider ยท 410 courses
Description
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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