Advanced Machine Learning with Python

Advanced Machine Learning with Python
by John Hearty
Print Length 页数: 278 pages
Publisher finelybook 出版社: Packt Publishing (28 July 2016)
Language 语言: English
ISBN-10: 1784398632
ISBN-13: 9781784398637
B019WV32LG
Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python
About This Book
Resolve complex machine learning problems and explore deep learning
Learn to use Python code for implementing a range of machine learning algorithms and techniques
A practical tutorial that tackles real-world computing problems through a rigorous and effective approach
Who This Book Is For
This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you’ve ever considered building your own image or text-tagging solution,or of entering a Kaggle contest for instance,this book is for you!
Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful.
What You Will Learn
Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms
Apply your new found skills to solve real problems,through clearly-explained code for every technique and test
Automate large sets of complex data and overcome time-consuming practical challenges
Improve the accuracy of models and your existing input data using powerful feature engineering techniques
Use multiple learning techniques together to improve the consistency of results
Understand the hidden structure of datasets using a range of unsupervised techniques
Gain insight into how the experts solve challenging data problems with an effective,iterative,and validation-focused approach
Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together
In Detail
Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists,this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques,semi-supervised learning,and more – all whilst working with real-world applications that include image,music,text,and financial data.
The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition,NLP and web search,computational creativity,and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies,but the skills needed to apply them successfully,while in high demand,are still scarce.
This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques,semi-supervised learning and more,in real world applications. We will also learn about NumPy and Theano.
By this end of this book,you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering.
Style and approach
This book focuses on clarifying the theory and code behind complex algorithms to make them practical,useable,and well-understood. Each topic is described with real-world applications,providing both broad contextual coverage and detailed guidance.
Contents
Chapter 1. Unsupervised Machine Learning
Chapter 2. Deep Belief Networks
Chapter 3. Stacked Denoising Autoencoders
Chapter 4. Convolutional Neural Networks
Chapter 5. Semi-Supervised Learning
Chapter 6. Text Feature Engineering
Chapter 7. Feature Engineering Part II
Chapter 8. Ensemble Methods
Chapter 9. Additional Python Machine Learning Tools
通过掌握Python中的尖端机器学习技术来解决具有挑战性的数据科学问题
关于这本书
解决复杂的机器学习问题，探索深入学习
学习使用Python代码来实现一系列机器学习算法和技术
一个实用的教程，通过严谨有效的方法解决现实世界的计算问题
这本书是谁
此标题适用于Python开发人员和分析师或数据科学家，他们希望通过访问数据科学中最强大的一些最新趋势来增加现有技能。如果您曾经考虑过建立自己的图像或文字标签解决方案，或者进入Kaggle比赛，这本书是为您而设的！
以前的Python经验和机器学习的一些核心概念的基础将是有帮助的。
你会学到什么
通过获得对尖端深度学习算法的实际和理论认识与顶尖数据科学家的竞争
应用您的新发现的技能来解决实际问题，通过对每种技术和测试的清晰解释的代码
自动化大量复杂数据，克服耗时的实践挑战
使用强大的功能工程技术提高模型的准确性和现有的输入数据
一起使用多种学习技巧来提高结果的一致性
使用一系列无监督技术了解数据集的隐藏结构
深入了解专家如何以有效，迭代和验证为重点的方法解决具有挑战性的数据问题
通过使用强大的组合技术将多个模型绑在一起，进一步提高您的深入学习模式的有效性
详细
这本书旨在为您带来最前沿的数据科学家今天使用的最相关和功能强大的机器学习技术的导游，这本书正是您将Python算法推向最大潜力所需要的。清晰的示例和详细的代码示例展示了深度学习技术，半监督学习和更多 – 同时使用包括图像，音乐，文本和财务数据在内的现实应用程序。
本书涵盖的机器学习技术处于商业实践的前沿。它们首次适用于图像识别，NLP和网络搜索，计算创意和商业/金融数据建模等领域。深度学习算法和模型集合正在由高科技和数字公司的数据科学家使用，但是在高需求的情况下成功应用所需的技能仍然很少。
本书旨在让读者参与最相关和强大的机器学习技术的导览。清楚描述技术的工作原理和详细的代码示例，在现实世界的应用中展示了深度学习技术，半监督学习等。我们还将了解NumPy和Theano。
在本书的这一端，您将学习一套先进的机器学习技术，并在特征选择和特征工程领域获得广泛的强大技能。
风格和方法
本书着重阐述复杂算法背后的理论和代码，使之具有实用性，可用性和理解力。每个主题都用现实世界的应用程序描述，提供广泛的上下文覆盖和详细的指导。
目录
第1章无监督机器学习
第二章深信仰网络
第3章堆叠去噪自动编码器
第四章卷积神经网络
第五章半监督学习
第六章文本特征工程
第七章特征工程第二部分
第八章合奏方法
其他Python机器学习工具

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