精通Python机器学习：深度学习与数据预处理

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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

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[ 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.

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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.

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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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