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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

You're reading from  Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

Product type Book
Published in Jul 2020
Publisher Packt
ISBN-13 9781838826048
Pages 384 pages
Edition 1st Edition
Languages
Author (1):
Tarek Amr Tarek Amr
Profile icon Tarek Amr
Toc

Table of Contents (18) Chapters close

Preface 1. Section 1: Supervised Learning
2. Introduction to Machine Learning 3. Making Decisions with Trees 4. Making Decisions with Linear Equations 5. Preparing Your Data 6. Image Processing with Nearest Neighbors 7. Classifying Text Using Naive Bayes 8. Section 2: Advanced Supervised Learning
9. Neural Networks – Here Comes Deep Learning 10. Ensembles – When One Model Is Not Enough 11. The Y is as Important as the X 12. Imbalanced Learning – Not Even 1% Win the Lottery 13. Section 3: Unsupervised Learning and More
14. Clustering – Making Sense of Unlabeled Data 15. Anomaly Detection – Finding Outliers in Data 16. Recommender System – Getting to Know Their Taste 17. Other Books You May Enjoy

Summary

Linear models are found everywhere. Their simplicity, as well as the capabilities they offer—such as regularization—makes them popular among practitioners. They also share many concepts with neural networks, which means that understanding them will help you in later chapters.

Being linear isn't usually a limiting factor as long as we can get creative with our feature transformation. Furthermore, in higher dimensions, the linearity assumption may hold more often than we think. That's why it is advised to always start with a linear model and then decide whether you need to go for a more advanced model.

Having that said, it can sometimesbe tricky to figure out the best configurations for your linear model or decide on which solver to use. In this chapter, we learned about using cross-validation to fine-tune a model's hyperparameters. We have also seen the different hyperparameters and solvers available, with tips for when to use...

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