Python: Real World Machine Learning

Book Description

Learn to solve challenging data science problems by building powerful machine learning models using Python

About This Book

• Understand which algorithms to use in a given context with the help of this exciting recipe-based guide
• This practical tutorial tackles real-world computing problems through a rigorous and effective approach
• Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale

Who This Book Is For

This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and 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. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected.

What You Will Learn

• Use predictive modeling and apply it to real-world problems
• Understand how to perform market segmentation using unsupervised learning
• Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test
• Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms
• Increase predictive accuracy with deep learning and scalable data-handling techniques
• Work with modern state-of-the-art large-scale machine learning techniques
• Learn to use Python code to implement a range of machine learning algorithms and techniques

In Detail

Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us.

In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you’ll acquire a broad set of powerful skills in the area of feature selection and feature engineering.

The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.

This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:

• Python Machine Learning Cookbook by Prateek Joshi
• Advanced Machine Learning with Python by John Hearty
• Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron

Style and approach

This course is a smooth learning path that will teach you how to get started with Python machine learning for the real world, and develop solutions to real-world problems. Through this comprehensive course, you’ll learn to create the most effective machine learning techniques from scratch and more!

Table of Contents

Part I. Module 1
Chapter 1. The Realm of Supervised Learning
Chapter 2. Constructing a Classifier
Chapter 3. Predictive Modeling
Chapter 4. Clustering with Unsupervised Learning
Chapter 5. Building Recommendation Engines
Chapter 6. Analyzing Text Data
Chapter 7. Speech Recognition
Chapter 8. Dissecting Time Series and Sequential Data
Chapter 9. Image Content Analysis
Chapter 10. Biometric Face Recognition
Chapter 11. Deep Neural Networks
Chapter 12. Visualizing Data

Part II. Module 2
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
Appendix A. Chapter Code Requirements

Part III. Module 3
Chapter 1. First Steps to Scalability
Chapter 2. Scalable Learning in Scikit-learn
Chapter 3. Fast SVM Implementations
Chapter 4. Neural Networks and Deep Learning
Chapter 5. Deep Learning with TensorFlow
Chapter 6. Classification and Regression Trees at Scale
Chapter 7. Unsupervised Learning at Scale
Chapter 8. Distributed Environments – Hadoop and Spark
Chapter 9. Practical Machine Learning with Spark
Appendix A. Introduction to GPUs and Theano

中文：

书名：Python: Real World Machine Learning

学习通过使用Python构建强大的机器学习模型来解决具有挑战性的数据科学问题

关于本书

• 在这个令人兴奋的基于食谱的指南的帮助下，了解在给定的上下文中应该使用哪些算法
• 本实用教程通过严格而有效的方法解决现实世界中的计算问题
• Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale

Who This Book Is For

本学习路径面向希望使用机器学习算法创建真实应用程序的Python程序员。它非常适合希望处理大型和复杂数据集的Python专业人员，以及希望通过访问数据科学中一些最强大的最新趋势来增加现有技能的Python开发人员和分析师或数据科学家。需要有使用Python、Jupyter笔记本和命令行执行的经验，以及理解概念的良好数学知识。机器学习的基本知识也是可取的。

你将学到什么

• 使用预测建模并将其应用于现实世界的问题
• 了解如何使用无监督学习进行市场细分
• 通过每项技术和测试的清晰解释的代码，应用您新发现的技能来解决实际问题
• 通过对尖端深度学习算法的实践和理论了解，与顶尖数据科学家竞争
• Increase predictive accuracy with deep learning and scalable data-handling techniques
• 使用现代最先进的大规模机器学习技术
• 学习使用Python代码实现一系列机器学习算法和技术

In Detail

在现代数据驱动的世界里，机器学习正日益普及。它被广泛应用于许多领域，如搜索引擎、机器人、自动驾驶汽车等。机器学习正在改变我们理解和与周围世界互动的方式。

在第一个模块《Python机器学习指南》中，您将学习如何使用各种机器学习算法来执行各种机器学习任务，以解决现实世界中的问题，并使用Python实现这些算法。

第二个模块，使用Python的高级机器学习，旨在带领您了解最相关和最强大的机器学习技术，您将在功能选择和功能工程领域获得广泛的强大技能。

本学习路径中的第三个模块–使用Python的大规模机器学习–深入探讨了可伸缩机器学习和三种形式的可伸缩性。它涵盖了Hadoop中的map Reduce框架和Python中的Spark上最有效的机器学习技术。

这条学习之路将教您如何在真实世界中使用Python机器学习。本学习路径中涵盖的机器学习技术处于商业实践的前沿。

此学习路径将Packt必须提供的一些最好的功能组合在一个完整的、经过精心策划的程序包中。它包括来自以下Packt产品的内容：

• Prateek Joshi著的《Python机器学习食谱》
• 约翰·哈蒂所著的用Python实现的高级机器学习
• Bastiaan Sjardin，Alberto Boschetti，Luca Massaron所著的大规模机器学习与Python

风格和方法

本课程是一条流畅的学习之路，将教您如何开始使用适用于真实世界的Python机器学习，并开发针对真实世界问题的解决方案。通过这门综合课程，您将学习从头开始创建最有效的机器学习技术，甚至更多！

目录表

第二部分。
第一章：监督学习的境界
第二章：构建量词
Chapter 3. Predictive Modeling
第四章基于无监督学习的数据聚类
第五章构建推荐引擎
Chapter 6. Analyzing Text Data
Chapter 7. Speech Recognition
Chapter 8. Dissecting Time Series and Sequential Data
第九章图片内容分析
第十章生物特征人脸识别
Chapter 11. Deep Neural Networks
Chapter 12. Visualizing Data

Part II. Module 2
第一章：无监督机器学习
Chapter 2. Deep Belief Networks
第三章.叠加式去噪自动编码器
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
第七章功能工程第二部分
Chapter 8. Ensemble Methods
第9章：附加的Python机器学习工具
附录A.章节代码要求

Part III. Module 3
Chapter 1. First Steps to Scalability
Chapter 2. Scalable Learning in Scikit-learn
第三章支持向量机的快速实现
Chapter 4. Neural Networks and Deep Learning
Chapter 5. Deep Learning with TensorFlow
第六章规模分类回归树
第七章规模的无监督学习
第8章：分布式环境-Hadoop和Spark
第九章：使用Spark的实用机器学习
附录A.GPU和Theano简介