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Python Machine Learning

You're reading from  Python Machine Learning

Product type Book
Published in Sep 2015
Publisher Packt
ISBN-13 9781783555130
Pages 454 pages
Edition 1st Edition
Languages
Author (1):
Sebastian Raschka Sebastian Raschka
Profile icon Sebastian Raschka

Table of Contents (21) Chapters

Python Machine Learning
Credits
Foreword
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Giving Computers the Ability to Learn from Data 2. Training Machine Learning Algorithms for Classification 3. A Tour of Machine Learning Classifiers Using Scikit-learn 4. Building Good Training Sets – Data Preprocessing 5. Compressing Data via Dimensionality Reduction 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning 7. Combining Different Models for Ensemble Learning 8. Applying Machine Learning to Sentiment Analysis 9. Embedding a Machine Learning Model into a Web Application 10. Predicting Continuous Target Variables with Regression Analysis 11. Working with Unlabeled Data – Clustering Analysis 12. Training Artificial Neural Networks for Image Recognition 13. Parallelizing Neural Network Training with Theano Index

First steps with scikit-learn


In Chapter 2, Training Machine Learning Algorithms for Classification, you learned about two related learning algorithms for classification: the perceptron rule and Adaline, which we implemented in Python by ourselves. Now we will take a look at the scikit-learn API, which combines a user-friendly interface with a highly optimized implementation of several classification algorithms. However, the scikit-learn library offers not only a large variety of learning algorithms, but also many convenient functions to preprocess data and to fine-tune and evaluate our models. We will discuss this in more detail together with the underlying concepts in Chapter 4, Building Good Training Sets – Data Preprocessing, and Chapter 5, Compressing Data via Dimensionality Reduction.

Training a perceptron via scikit-learn

To get started with the scikit-learn library, we will train a perceptron model similar to the one that we implemented in Chapter 2, Training Machine Learning Algorithms...

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