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Python Machine Learning

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

View More author details

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

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Authors (1)

Sebastian Raschka

Sebastian Raschka

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.

See other products by Sebastian Raschka

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