Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner.

This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages.

You’ll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you’ll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation.

All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.

What You'll Learn

• Examine the fundamentals of Python programming language
  
• Review machine Learning history and evolution
• Understand machine learning system development frameworks
• Implement supervised/unsupervised/reinforcement learning techniques with examples
• Explore fundamental to advanced text mining techniques
• Implement various deep learning frameworks

Who This Book Is For

Python developers or data engineers looking to expand their knowledge or career into machine learning area.

Non-Python (R, SAS, SPSS, Matlab or any other language) machine learning practitioners looking to expand their implementation skills in Python.

Novice machine learning practitioners looking to learn advanced topics, such as hyperparameter tuning, various ensemble techniques, natural language processing (NLP), deep learning, and basics of reinforcement learning.

用六个步骤掌握Python的机器学习，探索从基础到高级的课题，所有这些都是为了让你成为一个合格的实践者。

本书的方法是基于 "六度分隔 "理论，即每个人和每件事最多只有六步之遥。用Python掌握机器学习六步法》将每个主题分为两部分：理论概念和使用合适的Python软件包的实际实现。

你将学习Python编程语言的基础知识，机器学习的历史、演变和系统开发框架。关键的数据挖掘/分析概念，如特征降维、回归、时间序列预测和它们在Scikit-learn中的有效实现也包括在内。最后，你将探索高级文本挖掘技术、神经网络和深度学习技术，以及它们的实现。

书中介绍的所有代码都将以iPython笔记本的形式提供，使你能够尝试这些例子，并将它们扩展到你的优势。

你会学到什么

考察Python编程语言的基础知识

回顾机器学习的历史和演变

理解机器学习系统的开发框架

通过实例实现有监督/无监督/强化学习技术

探索从基础到高级的文本挖掘技术

实施各种深度学习框架

本书适用对象

希望在机器学习领域拓展知识或事业的Python开发人员或数据工程师。

非Python（R、SAS、SPSS、Matlab或任何其他语言）的机器学习从业者，希望在Python中扩展他们的实施技能。

希望学习高级课题的机器学习新手，如超参数调整、各种集合技术、自然语言处理（NLP）、深度学习和强化学习的基础知识。