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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
~ 12h
Level
Professional
Language
English
Edition
2025
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Chapters & sections

13 chapters - 54 sections

Chapter 1: Real-World Data Analysis Projects

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1.1 End-to-End Data Analysis: Healthcare Data22m1.2 Case Study: Retail Data and Customer Segmentation34m1.3 Practical Exercises for Chapter 15m1.4 What Could Go Wrong?5mChapter 1 Summary3m

Chapter 2: Feature Engineering for Predictive Modelscsv

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2.1 Predicting Customer Churn: Healthcare Data23m2.2 Feature Engineering for Classification and Regression Models35m2.3 Practical Exercises for Chapter 26m2.4 What Could Go Wrong?6m2.5 Chapter 2 Summary3m

Quiz Part 1: Practical Applications and Case Studies

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Questions3mAnswers1m

Project 1: Customer Segmentation using Clustering Techniques

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1. Understanding the K-means Clustering Algorithm13m2. Advanced Clustering Techniques18m3. Evaluating Clustering Results17m

Chapter 3: Automating Feature Engineering with Pipelines

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3.1 Pipelines in Scikit-learn: A Deep Dive22m3.2 Automating Data Preprocessing with FeatureUnion13m3.3 Practical Exercises for Chapter 37m3.4 What Could Go Wrong?6m3.5 Chapter 3 Summary3m

Chapter 4: Feature Engineering for Model Improvement

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4.1 Using Feature Importance to Guide Engineering21m4.2 Recursive Feature Elimination (RFE) and Model Tuning23m4.3 Practical Exercises for Chapter 46m4.4 What Could Go Wrong?6m4.5 Chapter 4 Summary3m

Chapter 5: Advanced Model Evaluation Techniques

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5.1 Cross-Validation Revisited: Stratified, Time-Series19m5.2 Dealing with Imbalanced Data: SMOTE, Class Weighting19m5.3 Practical Exercises for Chapter 56m5.4 What Could Go Wrong?6m5.5 Chapter 5 Summary4m

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

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Questions4mAnswers1m

Project 2: Feature Engineering with Deep Learning Models

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1.1 Leveraging Pretrained Models for Feature Extraction14m1.2 Integrating Deep Learning Features with Traditional Machine Learning Models16m1.3 Fine-Tuning Pretrained Models for Enhanced Feature Learning15m1.4 End-to-End Feature Learning in Hybrid Architectures16m1.5 Deployment Strategies for Hybrid Deep Learning Models14m

Chapter 6: Introduction to Feature Selection with Lasso and Ridge

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6.1 Regularization Techniques for Feature Selection27m6.2 Hyperparameter Tuning for Feature Engineering64m6.3 Practical Exercises: Chapter 66m6.4 What Could Go Wrong?4m6.5 Chapter 6 Summary3m

Chapter 7: Feature Engineering for Deep Learning

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7.1 Preparing Data for Neural Networks43m7.2 Integrating Feature Engineering with TensorFlow/Keras39m7.3 Practical Exercises: Chapter 77m7.4 What Could Go Wrong?4m7.5 Chapter 7 Summary3m

Chapter 8: AutoML and Automated Feature Engineering

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8.1 Exploring Automated Feature Engineering Tools21m8.2 Introduction to Feature Tools and AutoML Libraries28m8.3 Practical Exercises: Chapter 86m8.4 What Could Go Wrong?3m8.5 Chapter 8 Summary4m

Quiz Part 3: Advanced Topics and Future Trends

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Questions3mAnswers1m