Рет қаралды 3,995
In this comprehensive KZfaq video, we dive into the powerful world of feature transformation using the Column Transformer in Python's machine learning ecosystem. Whether you're a beginner or an experienced practitioner, understanding how to preprocess and transform features is crucial for building robust ML models.
Whether you're working on classification, regression, or any other machine learning task, mastering the art of feature transformation through the Column Transformer can significantly elevate your model's efficiency
Interested in discussing a Data or AI project? Feel free to reach out via email or simply complete the contact form on my website.
📧 Email: ryannolandata@gmail.com
🌐 Website & Blog: ryannolandata....
🍿 WATCH NEXT
Scikit-Learn and Machine Learning Playlist: • Scikit-Learn Tutorials...
Cross Validation: • A Comprehensive Guide ...
Feature Scaling: • Python Feature Scaling...
Simple Imputer: • Handling Missing Data ...
MY OTHER SOCIALS:
👨💻 LinkedIn: / ryan-p-nolan
🐦 Twitter: / ryannolan_
⚙️ GitHub: github.com/Rya...
🖥️ Discord: / discord
📚 *Data and AI Courses: datacamp.pxf.i...
📚 *Practice SQL & Python Interview Questions: stratascratch....
WHO AM I?
As a full-time data analyst/scientist at a fintech company specializing in combating fraud within underwriting and risk, I've transitioned from my background in Electrical Engineering to pursue my true passion: data. In this dynamic field, I've discovered a profound interest in leveraging data analytics to address complex challenges in the financial sector.
This KZfaq channel serves as both a platform for sharing knowledge and a personal journey of continuous learning. With a commitment to growth, I aim to expand my skill set by publishing 2 to 3 new videos each week, delving into various aspects of data analytics/science and Artificial Intelligence. Join me on this exciting journey as we explore the endless possibilities of data together.
*This is an affiliate program. I may receive a small portion of the final sale at no extra cost to you.