Mastering Machine Learning with Python in Six Steps

Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python
by: Manohar Swamynathan
ISBN-10: 1484228650
ISBN-13: 9781484228654
Edition 版本:‏ 1st ed.
Released: 2017-07-08
Pages: 358
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.
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
掌握掌握学习Python的六个步骤: 使用Python预测性数据分析的实用指南
主机学习与Python在六个步骤，探索基础到高级主题，所有旨在使您成为一个值得从业者。
这本书的方法是基于“六度分离”理论，其中指出，每个人和所有事物最多只有六步之遥。掌握机器学习的六个步骤中的Python将每个主题分为两个部分: 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，机器学习历史，演进和系统开发框架的基础知识。还介绍了关键数据挖掘/分析概念，如特征维度降低，回归，时间序列预测及其在Scikit学习中的有效实现。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。
本书中提供的所有代码将以iPython笔记本的形式提供，以使您能够尝试这些示例并将其扩展到您的优势。
你会学到什么
检查Python编程语言的基础知识
审查机器学习历史和进化
了解机器学习系统开发框架
通过实例实施监督/无监督/强化学习技术
探索高级文本挖掘技术的基础
实施各种深入学习框架
这本书是谁
Python开发人员或数据工程师希望将他们的知识或职业扩展到机器学习领域。
非Python（R，SAS，SPSS，Matlab或任何其他语言）机器学习从业者希望扩大其在Python中的实现技能。
新手学习从业者寻求学习高级课程，如超参数调整，各种合奏技巧，自然语言处理（NLP），深度学习和强化学习的基础知识。
目录
第1章: 第1步 – Python入门
第2章: 第2步 – 机器学习简介
第3章: 第3步 – 机器学习的基础
第4章: 第4步 – 模型诊断和调优
第5章: 第5步 – 文本挖掘与推荐系统
第6章: 第6步 – 深入和强化学习
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