Mastering Machine Learning with Python in Six Steps

Book Description

Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python

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.

Table of Contents

Chapter 1: Step 1 – Getting Started in Python
Chapter 2: Step 2 – Introduction to Machine Learning
Chapter 3: Step 3 – Fundamentals of Machine Learning
Chapter 4: Step 4 – Model Diagnosis and Tuning
Chapter 5: Step 5 – Text Mining and Recommender Systems
Chapter 6: Step 6 – Deep and Reinforcement Learning

中文：

书名：Mastering Machine Learning with Python in Six Steps

用六个步骤掌握机器学习：使用Python进行预测性数据分析的实用实施指南

通过六个步骤掌握使用Python的机器学习，并探索从基础到高级主题，所有这些都旨在让您成为一名有价值的实践者。

这本书的方法基于“六度分离”理论，该理论指出，每个人和每件事都最多离六步远。 用六个步骤掌握使用Python的机器学习 分两部分介绍每个主题：理论概念和使用合适的Python包的实际实现。

您将学习Python编程语言的基础知识、机器学习历史、进化和系统开发框架。文中还介绍了一些关键的数据挖掘/分析概念，如特征降维、回归、时间序列预测以及它们在Scikit-Learn中的有效实现。最后，您将探索高级文本挖掘技术、神经网络和深度学习技术及其实现。

书中提供的所有代码都将以IPython笔记本的形式提供，使您能够尝试这些示例并将其扩展为您的优势。

你会学到什么？

• 研究Python编程语言的基础知识
• 回顾机器学习的历史和发展
• 了解机器学习系统开发框架
• 通过示例实现有监督/无监督/强化学习技术
• 探索高级文本挖掘技术的基础知识
• 实施各种深度学习框架

这本书是为谁写的

希望将他们的知识或职业扩展到机器学习领域的Python开发人员或数据工程师。

非Python(R、SAS、SPSS、MatLab或任何其他语言)机器学习从业者，希望扩展他们在Python语言中的实现技能。

希望学习高级主题的新手机器学习从业者，如超参数调整、各种集成技术、自然语言处理(NLP)、深度学习和强化学习的基础知识。

目录表

Chapter 1: Step 1 – Getting Started in Python
第2章：第2步-机器学习简介
Chapter 3: Step 3 – Fundamentals of Machine Learning
第4章：第4步-模型诊断和调整
第5章：第5步-文本挖掘和推荐系统
第6章：第6步-深度学习和强化学习