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Feature Engineering for Modern Machine Learning with Scikit-Learn

13 chapters and 54 canonical sections synced from the Cuantum content database.

Author
Cuantum Tech.
Chapters
13
Reading time
~ 11h
Level
Professional
Language
English
Edition
2025
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Chapters & sections

13 chapters - 54 sections
Chapter 01

Chapter 1: Real-World Data Analysis Projects

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11.1 End-to-End Data Analysis: Healthcare Data12m21.2 Case Study: Retail Data and Customer Segmentation12m31.3 Practical Exercises for Chapter 112m41.4 What Could Go Wrong?12m5Chapter 1 Summary12m
Chapter 02

Chapter 2: Feature Engineering for Predictive Modelscsv

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12.1 Predicting Customer Churn: Healthcare Data12m22.2 Feature Engineering for Classification and Regression Models12m32.3 Practical Exercises for Chapter 212m42.4 What Could Go Wrong?12m52.5 Chapter 2 Summary12m
Chapter 03

Quiz Part 1: Practical Applications and Case Studies

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2Answers12m1Questions12m
Chapter 04

Project 1: Customer Segmentation using Clustering Techniques

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11. Understanding the K-means Clustering Algorithm12m22. Advanced Clustering Techniques12m33. Evaluating Clustering Results12m
Chapter 05

Chapter 3: Automating Feature Engineering with Pipelines

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13.1 Pipelines in Scikit-learn: A Deep Dive12m23.2 Automating Data Preprocessing with FeatureUnion12m33.3 Practical Exercises for Chapter 312m43.4 What Could Go Wrong?12m53.5 Chapter 3 Summary12m
Chapter 06

Chapter 4: Feature Engineering for Model Improvement

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14.1 Using Feature Importance to Guide Engineering12m24.2 Recursive Feature Elimination (RFE) and Model Tuning12m34.3 Practical Exercises for Chapter 412m44.4 What Could Go Wrong?12m54.5 Chapter 4 Summary12m
Chapter 07

Chapter 5: Advanced Model Evaluation Techniques

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15.1 Cross-Validation Revisited: Stratified, Time-Series12m25.2 Dealing with Imbalanced Data: SMOTE, Class Weighting12m35.3 Practical Exercises for Chapter 512m45.4 What Could Go Wrong?12m55.5 Chapter 5 Summary12m
Chapter 08

Quiz Part 2: Integration with Scikit-Learn for Model Building

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2Answers12m1Questions12m
Chapter 09

Project 2: Feature Engineering with Deep Learning Models

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11.1 Leveraging Pretrained Models for Feature Extraction12m21.2 Integrating Deep Learning Features with Traditional Machine Learning Models12m31.3 Fine-Tuning Pretrained Models for Enhanced Feature Learning12m41.4 End-to-End Feature Learning in Hybrid Architectures12m51.5 Deployment Strategies for Hybrid Deep Learning Models12m
Chapter 10

Chapter 6: Introduction to Feature Selection with Lasso and Ridge

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16.1 Regularization Techniques for Feature Selection12m26.2 Hyperparameter Tuning for Feature Engineering12m36.3 Practical Exercises: Chapter 612m46.4 What Could Go Wrong?12m56.5 Chapter 6 Summary12m
Chapter 11

Chapter 7: Feature Engineering for Deep Learning

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17.1 Preparing Data for Neural Networks12m27.2 Integrating Feature Engineering with TensorFlow/Keras12m37.3 Practical Exercises: Chapter 712m47.4 What Could Go Wrong?12m57.5 Chapter 7 Summary12m
Chapter 12

Chapter 8: AutoML and Automated Feature Engineering

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18.1 Exploring Automated Feature Engineering Tools12m28.2 Introduction to Feature Tools and AutoML Libraries12m38.3 Practical Exercises: Chapter 812m48.4 What Could Go Wrong?12m58.5 Chapter 8 Summary12m
Chapter 13

Quiz Part 3: Advanced Topics and Future Trends

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2Answers12m1Questions12m