Feature Engineering for Beginners
Chuck Sherman
Unravel the art and science behind effective data analysis with this comprehensive guide to feature engineering. Crafted for beginners, this book is your gateway to understanding the pivotal role of features in extracting meaningful insights from...
Location:
United States
Description:
Unravel the art and science behind effective data analysis with this comprehensive guide to feature engineering. Crafted for beginners, this book is your gateway to understanding the pivotal role of features in extracting meaningful insights from data. From the basics of feature engineering to hands-on techniques, this guide navigates through the intricate landscape of transforming raw data into powerful features. You'll explore the fundamental principles that underpin feature engineering and gain practical skills through real-world examples and case studies. Equip yourself with the essential skills to transform raw data into actionable insights. 'Feature Engineering for Beginners' is your companion in the journey towards mastering the craft of feature engineering and unleashing the true potential of your data analysis endeavors. Duration - 11h 1m. Author - Chuck Sherman. Narrator - Ray Collins. Published Date - Saturday, 03 January 2026. Copyright - © 2025 Khin Soe ©.
Language:
English
Opening Credits
Duration:00:00:06
Introduction
Duration:00:00:33
Chapter 1 the foundation of feature
Duration:00:15:23
Chapter 2 types of features
Duration:00:43:11
Chapter 3 handling missing data
Duration:01:08:24
Chapter 4 feature transformation
Duration:00:13:59
Chapter 5 feature selection
Duration:00:16:32
Chapter 6 feature engineering for
Duration:00:15:29
Chapter 7 advanced feature engineering
Duration:00:15:33
Chapter 8 putting it all together
Duration:00:29:01
Conclusion
Duration:00:00:36
Opening credits
Duration:00:00:13
Chapter 1 foundations of machine
Duration:00:27:04
Chapter 2 data preprocessing and
Duration:00:09:56
Chapter 3 feature engineering
Duration:00:08:55
Chapter 4 model selection and
Duration:00:26:23
Chapter 5 building scalable machine
Duration:00:22:39
Chapter 6 model deployment and
Duration:00:33:32
Chapter 7 interpretable machine learning
Duration:00:08:31
Chapter 8 handling streaming data in
Duration:00:52:01
Chapter 9 ethical considerations and
Duration:00:13:00
Chapter 10 future trends in machine
Duration:00:41:04
The impact on model performance
Duration:00:33:54
Methods of scaling data
Duration:00:13:58
Challenges and pitfalls in data scaling
Duration:00:11:56
Normalization techniques
Duration:00:12:03
Advanced techniques in data
Duration:00:11:02
Implementing data scaling and
Duration:00:14:26
Best practices and tips for data
Duration:00:10:05
Future trends in data scaling and
Duration:00:53:27
Case studies
Duration:00:09:45
Foundations of data scaling and
Duration:00:25:24
Ending Credits
Duration:00:00:08