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scikit-learn Cookbook , Second Edition

You're reading from scikit-learn Cookbook , Second Edition Over 80 recipes for machine learning in Python with scikit-learn

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781787286382

Length 374 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Trent Hauck

View More author details

Table of Contents (13) Chapters

Preface

1. High-Performance Machine Learning – NumPy FREE CHAPTER

2. Pre-Model Workflow and Pre-Processing

3. Dimensionality Reduction

4. Linear Models with scikit-learn

5. Linear Models – Logistic Regression

6. Building Models with Distance Metrics

7. Cross-Validation and Post-Model Workflow

8. Support Vector Machines

9. Tree Algorithms and Ensembles

10. Text and Multiclass Classification with scikit-learn

11. Neural Networks

12. Create a Simple Estimator

Building Models with Distance Metrics

This chapter will cover the following recipes:

Using k-means to cluster data
Optimizing the number of centroids
Assessing cluster correctness
Using MiniBatch k-means to handle more data
Quantizing an image with k-means clustering
Finding the closest objects in the feature space
Probabilistic clustering with Gaussian Mixture Models
Using k-means for outlier detection
Using KNN for regression

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Authors (1)

Trent Hauck

Trent Hauck

Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas. He is the author of the book Instant Data Intensive Apps with pandas How-to, Packt Publishing—a book that can get you up to speed quickly with pandas and other associated technologies.

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