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You're reading from Python Machine Learning Learn how to build powerful Python machine learning algorithms to generate useful data insights with this data analysis tutorial

Product type Paperback

Published in Sep 2015

Publisher Packt

ISBN-13 9781783555130

Length 454 pages

Edition 1st Edition

Languages

Python

Tools

SciPy

Concepts

Machine Learning

Author (1):

Sebastian Raschka

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Table of Contents (15) Chapters

Preface

1. Giving Computers the Ability to Learn from Data

2. Training Machine Learning Algorithms for Classification FREE CHAPTER

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...