Feature Engineering from Scratch

De: Ajit Singh

Narrado por: Virtual Voice

"Feature Engineering from Scratch" is a comprehensive guide designed to equip students, academics, and practitioners with the essential skills to master the art and science of feature engineering. It provides a structured, hands-on, and intuitive pathway to understanding how to transform raw data into powerful, predictive features that fuel high-performance machine learning models.


Key Features:


1. Progressive Learning Curve: Carefully structured to guide learners from beginner-level concepts to advanced topics, making it suitable for a wide audience.
2. Hands-On Practical Implementation: Every technique is accompanied by working Python code, enabling readers to immediately apply what they learn.
3. Real-World Case Studies: Includes mini-case studies throughout the chapters to demonstrate the impact of feature engineering on actual machine learning problems.
3. Intuition-First Approach: Complex topics are broken down into simple, easy-to-understand components, building a strong conceptual foundation.
4. End-to-End Capstone Project: A dedicated final chapter guides the reader through a complete DIY project, from data cleaning and feature engineering to model building and evaluation.


To Whom This Book Is For:


1. B.Tech/M.Tech Computer Science Students: An ideal textbook for courses on Machine Learning, Data Science, or Artificial Intelligence, providing both theoretical knowledge and practical lab-ready exercises.
2. Aspiring Data Scientists and ML Engineers: A perfect self-study guide to build one of the most critical and sought-after skills in the industry.
3. Software Developers: A clear and practical resource for developers looking to transition into the field of AI/ML.
4. University Professors and Educators: A well-structured, syllabus-compliant resource for designing and teaching courses on practical machine learning.
5. Data Analysts: A valuable guide for analysts who want to enhance their skill set and move beyond traditional data analysis to predictive modeling.


The core philosophy is "learning by doing." Every chapter is replete with clear explanations, real-world analogies, and practical Python code examples using popular libraries like Pandas, Scikit-learn, and Matplotlib. The focus is not just on how to implement a technique, but on why it works and when to use it.