Python机器学习实战：深度探索预测性分析

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"Python Machine Learning 是一本面向初学者和进阶者的指南，旨在帮助读者深入理解机器学习，并利用Python的开源库进行深度学习、数据处理和数据可视化。本书适合想要利用Python解答关键数据问题的读者，无论你是初涉数据科学还是寻求扩展知识，都能从中受益。书中将探讨如何运用不同机器学习模型对数据提问，构建神经网络，编写优化算法的优雅Python代码，以及将机器学习模型嵌入Web应用等。同时，涵盖回归分析预测连续目标结果，聚类发现数据中的隐藏模式，以及有效预处理数据和进行情感分析等内容。通过理论与实践相结合的方式，让你掌握机器学习的核心原理和Python的相关库，如scikit-learn、Theano和Pylearn2。" 在这本书中，读者将学习到： 1. 机器学习模型的运用：了解如何根据问题选择合适的机器学习模型，例如分类、回归、聚类等，以揭示数据中的趋势和模式。 2. 深度学习框架：学习使用Pylearn 2和Theano构建神经网络，这是实现复杂预测和图像识别任务的关键技术。 3. Python编码技巧：编写高效且易于维护的Python代码，提升算法性能，这对于优化机器学习系统至关重要。 4. 模型部署：学习如何将训练好的机器学习模型集成到Web应用程序中，提高模型的可用性和影响力。 5. 回归分析：运用回归分析来预测连续型目标变量，例如预测销售量或股票价格。 6. 聚类分析：通过聚类方法发现数据集中的隐藏结构，帮助识别不同的用户群体或市场细分。 7. 数据预处理：学习预处理技术，如特征缩放、缺失值处理和异常值检测，以提高模型的准确性和稳定性。 8. 情感分析：探索如何处理文本和社交媒体数据，进行情感分析，理解公众对产品或服务的态度。 9. 理论与实践的结合：书中的讲解不仅涵盖了机器学习的基础理论，还提供了实用的代码示例，帮助读者将理论应用到实际项目中。 10. 关键库的使用：深入理解并熟练运用Python的机器学习库scikit-learn，以及用于深度学习的Theano和Pylearn2，这些库在数据科学领域有着广泛的应用。《Python Machine Learning》是那些希望利用Python进行机器学习和预测分析的读者的理想资源，无论你是数据科学的新手还是有经验的专业人士，这本书都能提供丰富的知识和实践经验，助你在数据驱动的世界中取得成功。

Table of Contents

[ v ]

Chapter 10: Predicting Continuous Target Variables

with Regression Analysis 277

Introducing a simple linear regression model 278

Exploring the Housing Dataset 279

Visualizing the important characteristics of a dataset 280

Implementing an ordinary least squares linear regression model 285

Solving regression for regression parameters with gradient descent 285

Estimating the coefcient of a regression model via scikit-learn 289

Fitting a robust regression model using RANSAC 291

Evaluating the performance of linear regression models 294

Using regularized methods for regression 297

Turning a linear regression model into a curve – polynomial

regression 298

Modeling nonlinear relationships in the Housing Dataset 300

Dealing with nonlinear relationships using random forests 304

Decision tree regression 304

Random forest regression 306

Summary 309

Chapter 11: Working with Unlabeled Data – Clustering Analysis 311

Grouping objects by similarity using k-means 312

K-means++ 315

Hard versus soft clustering 317

Using the elbow method to nd the optimal number of clusters 320

Quantifying the quality of clustering via silhouette plots 321

Organizing clusters as a hierarchical tree 326

Performing hierarchical clustering on a distance matrix 328

Attaching dendrograms to a heat map 332

Applying agglomerative clustering via scikit-learn 334

Locating regions of high density via DBSCAN 334

Summary 340

Chapter 12: Training Articial Neural Networks for Image

Recognition 341

Modeling complex functions with articial neural networks 342

Single-layer neural network recap 343

Introducing the multi-layer neural network architecture 345

Activating a neural network via forward propagation 347

[ vii ]

Preface

I probably don't need to tell you that machine learning has become one of the most

exciting technologies of our time and age. Big companies, such as Google, Facebook,

Apple, Amazon, IBM, and many more, heavily invest in machine learning research

and applications for good reasons. Although it may seem that machine learning has

become the buzzword of our time and age, it is certainly not a hype. This exciting

eld opens the way to new possibilities and has become indispensable to our daily

lives. Talking to the voice assistant on our smart phones, recommending the right

product for our customers, stopping credit card fraud, ltering out spam from our

e-mail inboxes, detecting and diagnosing medical diseases, the list goes on and on.

If you want to become a machine learning practitioner, a better problem solver, or

maybe even consider a career in machine learning research, then this book is for you!

However, for a novice, the theoretical concepts behind machine learning can be quite

overwhelming. Yet, many practical books that have been published in recent years

will help you get started in machine learning by implementing powerful learning

algorithms. In my opinion, the use of practical code examples serve an important

purpose. They illustrate the concepts by putting the learned material directly into

action. However, remember that with great power comes great responsibility! The

concepts behind machine learning are too beautiful and important to be hidden in

a black box. Thus, my personal mission is to provide you with a different book; a

book that discusses the necessary details regarding machine learning concepts, offers

intuitive yet informative explanations on how machine learning algorithms work,

how to use them, and most importantly, how to avoid the most common pitfalls.

If you type "machine learning" as a search term in Google Scholar, it returns an

overwhelmingly large number-1,800,000 publications. Of course, we cannot discuss

all the nitty-gritty details about all the different algorithms and applications that have

emerged in the last 60 years. However, in this book, we will embark on an exciting

journey that covers all the essential topics and concepts to give you a head start in this

eld. If you nd that your thirst for knowledge is not satised, there are many useful

resources that can be used to follow up on the essential breakthroughs in this eld.

Preface

[ viii ]

If you have already studied machine learning theory in detail, this book will show

you how to put your knowledge into practice. If you have used machine learning

techniques before and want to gain more insight into how machine learning really

works, this book is for you! Don't worry if you are completely new to the machine

learning eld; you have even more reason to be excited. I promise you that machine

learning will change the way you think about the problems you want to solve and

will show you how to tackle them by unlocking the power of data.

Before we dive deeper into the machine learning eld, let me answer your most

important question, "why Python?" The answer is simple: it is powerful yet very

accessible. Python has become the most popular programming language for data

science because it allows us to forget about the tedious parts of programming and

offers us an environment where we can quickly jot down our ideas and put concepts

directly into action.

Reecting on my personal journey, I can truly say that the study of machine learning

made me a better scientist, thinker, and problem solver. In this book, I want to

share this knowledge with you. Knowledge is gained by learning, the key is our

enthusiasm, and the true mastery of skills can only be achieved by practice. The road

ahead may be bumpy on occasions, and some topics may be more challenging than

others, but I hope that you will embrace this opportunity and focus on the reward.

Remember that we are on this journey together, and throughout this book, we will

add many powerful techniques to your arsenal that will help us solve even the

toughest problems the data-driven way.

What this book covers

Chapter 1, Giving Computers the Ability to Learn from Data, introduces you to the

main subareas of machine learning to tackle various problem tasks. In addition, it

discusses the essential steps for creating a typical machine learning model building

pipeline that will guide us through the following chapters.

Chapter 2, Training Machine Learning Algorithms for Classication, goes back to

the origin of machine learning and introduces binary perceptron classiers and

adaptive linear neurons. This chapter is a gentle introduction to the fundamentals

of pattern classication and focuses on the interplay of optimization algorithms and

machine learning.

Chapter 3, A Tour of Machine Learning Classirs Using Scikit-learn, describes the

essential machine learning algorithms for classication and provides practical

examples using one of the most popular and comprehensive open source machine

learning libraries, scikit-learn.

Preface

[ ix ]

Chapter 4, Building Good Training Sets – Data Preprocessing, discusses how to deal with

the most common problems in unprocessed datasets, such as missing data. It also

discusses several approaches to identify the most informative features in datasets

and teaches you how to prepare variables of different types as proper inputs for

machine learning algorithms.

Chapter 5, Compressing Data via Dimensionality Reduction, describes the essential

techniques to reduce the number of features in a dataset to smaller sets while

retaining most of their useful and discriminatory information. It discusses the

standard approach to dimensionality reduction via principal component analysis

and compares it to supervised and nonlinear transformation techniques.

Chapter 6, Learning Best Practices for Model Evaluation and Hyperparameter Tuning,

discusses the do's and don'ts for estimating the performances of predictive models.

Moreover, it discusses different metrics for measuring the performance of our

models and techniques to ne-tune machine learning algorithms.

Chapter 7, Combining Different Models for Ensemble Learning, introduces you to the

different concepts of combining multiple learning algorithms effectively. It teaches

you how to build ensembles of experts to overcome the weaknesses of individual

learners, resulting in more accurate and reliable predictions.

Chapter 8, Applying Machine Learning to Sentiment Analysis, discusses the essential

steps to transform textual data into meaningful representations for machine learning

algorithms to predict the opinions of people based on their writing.

Chapter 9, Embedding a Machine Learning Model into a Web Application, continues with

the predictive model from the previous chapter and walks you through the essential

steps of developing web applications with embedded machine learning models.

Chapter 10, Predicting Continuous Target Variables with Regression Analysis, discusses

the essential techniques for modeling linear relationships between target and

response variables to make predictions on a continuous scale. After introducing

different linear models, it also talks about polynomial regression and

tree-based approaches.

Chapter 11, Working with Unlabeled Data – Clustering Analysis, shifts the focus to a

different subarea of machine learning, unsupervised learning. We apply algorithms

from three fundamental families of clustering algorithms to nd groups of objects

that share a certain degree of similarity.

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