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Data Cleaning and Exploration with Machine Learning

You're reading from   Data Cleaning and Exploration with Machine Learning Get to grips with machine learning techniques to achieve sparkling-clean data quickly

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781803241678
Length 542 pages
Edition 1st Edition
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Author (1):
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Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
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Table of Contents (23) Chapters Close

Preface 1. Section 1 – Data Cleaning and Machine Learning Algorithms
2. Chapter 1: Examining the Distribution of Features and Targets FREE CHAPTER 3. Chapter 2: Examining Bivariate and Multivariate Relationships between Features and Targets 4. Chapter 3: Identifying and Fixing Missing Values 5. Section 2 – Preprocessing, Feature Selection, and Sampling
6. Chapter 4: Encoding, Transforming, and Scaling Features 7. Chapter 5: Feature Selection 8. Chapter 6: Preparing for Model Evaluation 9. Section 3 – Modeling Continuous Targets with Supervised Learning
10. Chapter 7: Linear Regression Models 11. Chapter 8: Support Vector Regression 12. Chapter 9: K-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosted Regression 13. Section 4 – Modeling Dichotomous and Multiclass Targets with Supervised Learning
14. Chapter 10: Logistic Regression 15. Chapter 11: Decision Trees and Random Forest Classification 16. Chapter 12: K-Nearest Neighbors for Classification 17. Chapter 13: Support Vector Machine Classification 18. Chapter 14: Naïve Bayes Classification 19. Section 5 – Clustering and Dimensionality Reduction with Unsupervised Learning
20. Chapter 15: Principal Component Analysis 21. Chapter 16: K-Means and DBSCAN Clustering 22. Other Books You May Enjoy

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