A Guide For Data Scientists
by Andreas C. Müller & Sarah Guido
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Book Details
Price
|
3.00 USD |
|---|---|
Pages
| 392 p |
File Size
|
32,382 KB |
File Type
|
PDF format |
ISBN
| 978-1-449-36941-5 |
Copyright
| 2017 Sarah Guido, Andreas Müller |
Machine learning is an integral part of many commercial applications and research
projects today, in areas ranging from medical diagnosis and treatment to finding your
friends on social networks. Many people think that machine learning can only be
applied by large companies with extensive research teams. In this book, we want to
show you how easy it can be to build machine learning solutions yourself, and how to
best go about it. With the knowledge in this book, you can build your own system for
finding out how people feel on Twitter, or making predictions about global warming.
The applications of machine learning are endless and, with the amount of data available
today, mostly limited by your imagination.
Who Should Read This Book
This book is for current and aspiring machine learning practitioners looking to
implement solutions to real-world machine learning problems. This is an introductory
book requiring no previous knowledge of machine learning or artificial intelligence
(AI). We focus on using Python and the scikit-learn library, and work
through all the steps to create a successful machine learning application. The methods
we introduce will be helpful for scientists and researchers, as well as data scientists
working on commercial applications. You will get the most out of the book if you
are somewhat familiar with Python and the NumPy and matplotlib libraries.
We made a conscious effort not to focus too much on the math, but rather on the
practical aspects of using machine learning algorithms. As mathematics (probability
theory, in particular) is the foundation upon which machine learning is built, we
won’t go into the analysis of the algorithms in great detail. If you are interested in the
mathematics of machine learning algorithms, we recommend the book The Elements
of Statistical Learning (Springer) by Trevor Hastie, Robert Tibshirani, and Jerome
Friedman, which is available for free at the authors’ website. We will also not describe
how to write machine learning algorithms from scratch, and will instead focus on
how to use the large array of models already implemented in scikit-learn and other libraries.
Navigating This Book
This book is organized roughly as follows:
• Chapter 1 introduces the fundamental concepts of machine learning and its
applications, and describes the setup we will be using throughout the book.
• Chapters 2 and 3 describe the actual machine learning algorithms that are most
widely used in practice, and discuss their advantages and shortcomings.
• Chapter 4 discusses the importance of how we represent data that is processed by
machine learning, and what aspects of the data to pay attention to.
• Chapter 5 covers advanced methods for model evaluation and parameter tuning,
with a particular focus on cross-validation and grid search.
• Chapter 6 explains the concept of pipelines for chaining models and encapsulating
your workflow.
• Chapter 7 shows how to apply the methods described in earlier chapters to text
data, and introduces some text-specific processing techniques.
• Chapter 8 offers a high-level overview, and includes references to more advanced topics.
While Chapters 2 and 3 provide the actual algorithms, understanding all of these
algorithms might not be necessary for a beginner. If you need to build a machine
learning system ASAP, we suggest starting with Chapter 1 and the opening sections of
Chapter 2, which introduce all the core concepts. You can then skip to “Summary and
Outlook” on page 127 in Chapter 2, which includes a list of all the supervised models
that we cover. Choose the model that best fits your needs and flip back to read the
section devoted to it for details. Then you can use the techniques in Chapter 5 to evaluate
and tune your model.
Table of Contents
Preface vii
1. Introduction 1
Why Machine Learning? 1
Problems Machine Learning Can Solve 2
Knowing Your Task and Knowing Your Data 4
Why Python? 5
scikit-learn 5
Installing scikit-learn 6
Essential Libraries and Tools 7
Jupyter Notebook 7
NumPy 7
SciPy 8
matplotlib 9
pandas 10
mglearn 11
Python 2 Versus Python 3 12
Versions Used in this Book 12
A First Application: Classifying Iris Species 13
Meet the Data 14
Measuring Success: Training and Testing Data 17
First Things First: Look at Your Data 19
Building Your First Model: k-Nearest Neighbors 20
Making Predictions 22
Evaluating the Model 22
Summary and Outlook 23
2. Supervised Learning. 25
Classification and Regression 25
Generalization, Overfitting, and Underfitting 26
Relation of Model Complexity to Dataset Size 29
Supervised Machine Learning Algorithms 29
Some Sample Datasets 30
k-Nearest Neighbors 35
Linear Models 45
Naive Bayes Classifiers 68
Decision Trees 70
Ensembles of Decision Trees 83
Kernelized Support Vector Machines 92
Neural Networks (Deep Learning) 104
Uncertainty Estimates from Classifiers 119
The Decision Function 120
Predicting Probabilities 122
Uncertainty in Multiclass Classification 124
Summary and Outlook 127
3. Unsupervised Learning and Preprocessing . 131
Types of Unsupervised Learning 131
Challenges in Unsupervised Learning 132
Preprocessing and Scaling 132
Different Kinds of Preprocessing 133
Applying Data Transformations 134
Scaling Training and Test Data the Same Way 136
The Effect of Preprocessing on Supervised Learning 138
Dimensionality Reduction, Feature Extraction, and Manifold Learning 140
Principal Component Analysis (PCA) 140
Non-Negative Matrix Factorization (NMF) 156
Manifold Learning with t-SNE 163
Clustering 168
k-Means Clustering 168
Agglomerative Clustering 182
DBSCAN 187
Comparing and Evaluating Clustering Algorithms 191
Summary of Clustering Methods 207
Summary and Outlook 208
4. Representing Data and Engineering Features . 211
Categorical Variables 212
One-Hot-Encoding (Dummy Variables) 213
Numbers Can Encode Categoricals 218
Binning, Discretization, Linear Models, and Trees 220
Interactions and Polynomials 224
Univariate Nonlinear Transformations 232
Automatic Feature Selection 236
Univariate Statistics 236
Model-Based Feature Selection 238
Iterative Feature Selection 240
Utilizing Expert Knowledge 242
Summary and Outlook 250
5. Model Evaluation and Improvement . 251
Cross-Validation 252
Cross-Validation in scikit-learn 253
Benefits of Cross-Validation 254
Stratified k-Fold Cross-Validation and Other Strategies 254
Grid Search 260
Simple Grid Search 261
The Danger of Overfitting the Parameters and the Validation Set 261
Grid Search with Cross-Validation 263
Evaluation Metrics and Scoring 275
Keep the End Goal in Mind 275
Metrics for Binary Classification 276
Metrics for Multiclass Classification 296
Regression Metrics 299
Using Evaluation Metrics in Model Selection 300
Summary and Outlook 302
6. Algorithm Chains and Pipelines . 305
Parameter Selection with Preprocessing 306
Building Pipelines 308
Using Pipelines in Grid Searches 309
The General Pipeline Interface 312
Convenient Pipeline Creation with make_pipeline 313
Accessing Step Attributes 314
Accessing Attributes in a Grid-Searched Pipeline 315
Grid-Searching Preprocessing Steps and Model Parameters 317
Grid-Searching Which Model To Use 319
Summary and Outlook 320
7. Working with Text Data . . 323
Types of Data Represented as Strings 323
Example Application: Sentiment Analysis of Movie Reviews 325
Representing Text Data as a Bag of Words 327
Applying Bag-of-Words to a Toy Dataset 329
Bag-of-Words for Movie Reviews 330
Stopwords 334
Rescaling the Data with tf–idf 336
Investigating Model Coefficients 338
Bag-of-Words with More Than One Word (n-Grams) 339
Advanced Tokenization, Stemming, and Lemmatization 344
Topic Modeling and Document Clustering 347
Latent Dirichlet Allocation 348
Summary and Outlook 355
8. Wrapping Up . 357
Approaching a Machine Learning Problem 357
Humans in the Loop 358
From Prototype to Production 359
Testing Production Systems 359
Building Your Own Estimator 360
Where to Go from Here 361
Theory 361
Other Machine Learning Frameworks and Packages 362
Ranking, Recommender Systems, and Other Kinds of Learning 363
Probabilistic Modeling, Inference, and Probabilistic Programming 363
Neural Networks 364
Scaling to Larger Datasets 364
Honing Your Skills 365
Conclusion 366
Index 367
Bookscreen
Introduction
There are many books on machine learning and AI. However, all of them are meant
for graduate students or PhD students in computer science, and they’re full of
advanced mathematics. This is in stark contrast with how machine learning is being
used, as a commodity tool in research and commercial applications. Today, applying
machine learning does not require a PhD. However, there are few resources out there
that fully cover all the important aspects of implementing machine learning in practice,
without requiring you to take advanced math courses. We hope this book will
help people who want to apply machine learning without reading up on years’ worth
of calculus, linear algebra, and probability theory.