Rescaling is a common preprocessing task in machine learning. Many of the algorithms described later in this book will assume all features are on the same scale, typically 0 to 1 or –1 to 1. There are a number
This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data...
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NumPy is the foundation of the Python machine learning stack. NumPy allows for efficient operations on the data structures often used in machine learning: vectors, matrices, and tensors. While NumPy is not the focus of this book, it will show up frequently throughout the following chapters. Th...
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Chapter 1, The Realm of Supervised Learning, covers various machine learning paradigms that will help you to understand how the field is divided into multiple subgroups. This chapter briefly discuss the differences between supervised and unsupervised learning, along with the concepts of regression, clas...
为了确保我们所使用的技术栈能够无缝连接,我分析了“Machine Learning with Python Cookbook”中涵盖的库和工具,并绘制了技术栈兼容性的四象限图。 quadrantChart title 技术栈兼容性 x-axis 不兼容 --> 兼容 y-axis 低价值 --> 高价值 "NumPy": [0.8, 0.9] ...
This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks.With the help of over 100 recipes, you will learn to bui
Python Machine Learning Cookbook Copyright © 2016 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief ...
A Naive Bayes classifier is a supervised learning classifier that uses Bayes' theorem to build the model. Let's go ahead and build a Naïve Bayes classifier.How to do it… We will use naive_bayes.py that is provided to you as reference. Let's import a couple of things: from sk...