Python机器学习
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作者(美)塞巴斯蒂安·拉什卡(Sebastian Raschka) 著
出版社东南大学出版社
ISBN9787564170776
出版时间2017-04
版次1
装帧平装
开本16开
纸张胶版纸
页数425页
字数553千字
定价87元
货号SC:9787564170776
上书时间2024-07-09
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作者简介:
Sebastian Raschka is a PhD student at Michigan State University, who develops new computational methods in the field of computational biology. He has been ranked as the number one most influential data scientist on GitHub by Analytics Vidhya. He has a yearlong experience in Python programming and he has conducted several seminars on the practical applications of data science and machine learning. Talking and writing about data science, machine learning, and Python really motivated Sebastian to write this book in order to help people develop data-driven solutions without necessarily needing to have a machine learning background.
He has also actively contributed to open source projects and methods that he implemented, which are now successfully used in machine learning competitions, such as Kaggle. In his free time, he works on models for sports predictions, and if he is not in front of the computer, he enjoys playing sports.
内容简介:
本书将带你进入预测分析的世界，通过演示告诉你为什么Python是世界很好的数据科学语言之一。如果你想询问更深入的数据问题，或是想增进、拓展机器学习系统的能力，这本实用的书籍可谓是无价之宝。书中涵盖了包括 scikit-learn、Theano和Keras在内的大量功能强大的Python库，操作指南以及从情感分析到神经网络的各色小技巧，很快你就能够解答你个人及组织所面对的那些最重要的问题。
目录:
Preface
Chapter 1： Givin Computers the Ability to Learn from Data
Building intelligent machines to transform data into knowledge
The three different types of machine learning
Making predictions about the future with supervised learning
Classification for predicting class labels
Regression for predicting continuous outcomes
Solving interactive problems with reinforcement learning
Discovering hidden structures with unsupervised learning
Finding subgroups with clustering
Dimensionality reduction for data compression
An introduction to the basic terminology and notations
A roadmap for building machine learning systems
Preprocessing-getting data into shape
Training and selecting a predictive model
Evaluati
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