Introducing Our Course Feature Engineering for Machine Learning скачать в хорошем качестве

Introducing Our Course Feature Engineering for Machine Learning

In this course, you'll learn how to preprocess your data to fill missing values, encode categorical variables, change variable distributions and scales, work with date and time, and much more, leaving your datasets ready to train supervised regression or classification machine learning models. What you’ll learn: ➡️ Foundational Techniques: Impute missing data, encode categorical variables, and scale numerical features. ➡️ Advanced Methods: Automate feature creation with Feature-engine, apply decision trees for variable transformation, and extract features from dates and time. ➡️ Practical Applications: Implement workflows using Python libraries such as Pandas, Scikit-learn, and Feature-engine. ➡️ Gain confidence with 100+ lectures, real-world datasets, and Python code examples. Build end-to-end machine learning pipelines ready for production. Move beyond basics with cutting-edge techniques from real-world applications, competitions, and research. Implement robust workflows using Python libraries like Pandas, Scikit-learn, and Feature-engine. Taught by Sole, a LinkedIn Top Voice in Data Science, and author of the Python Feature Engineering Cookbook. Her expertise ensures you gain not only knowledge but also practical insights directly applicable to your career. Transform your machine learning workflows with the industry's most in-depth feature engineering course. Enroll now: https://www.trainindata.com/p/feature...