Python Machine Learning, Second Edition E-Book

Sebastian Raschka

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch
• Veröffentlichungsjahr: 2017

Seitenzahl: 692

Beschreibung

Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published.

Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.

Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library.

Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities.

If you’ve read the first edition of this book, you’ll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.

Leseprobe

Table of Contents

Python Machine Learning Second Edition

Credits

About the Authors

About the Reviewers

www.PacktPub.com

eBooks, discount offers, and more

Why subscribe?

Packt is Searching for Authors Like You

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Giving Computers the Ability to Learn from Data

Building intelligent machines to transform data into knowledge

The three different types of machine learning

Making predictions about the future with supervised learning

Classification for predicting class labels

Regression for predicting continuous outcomes

Solving interactive problems with reinforcement learning

Discovering hidden structures with unsupervised learning

Finding subgroups with clustering

Dimensionality reduction for data compression

Introduction to the basic terminology and notations

A roadmap for building machine learning systems

Preprocessing – getting data into shape

Training and selecting a predictive model

Evaluating models and predicting unseen data instances

Using Python for machine learning

Installing Python and packages from the Python Package Index

Using the Anaconda Python distribution and package manager

Packages for scientific computing, data science, and machine learning

Summary

2. Training Simple Machine Learning Algorithms for Classification

Artificial neurons – a brief glimpse into the early history of machine learning

The formal definition of an artificial neuron

The perceptron learning rule

Implementing a perceptron learning algorithm in Python

An object-oriented perceptron API

Training a perceptron model on the Iris dataset

Adaptive linear neurons and the convergence of learning

Minimizing cost functions with gradient descent

Implementing Adaline in Python

Improving gradient descent through feature scaling

Large-scale machine learning and stochastic gradient descent

Summary

3. A Tour of Machine Learning Classifiers Using scikit-learn

Choosing a classification algorithm

First steps with scikit-learn – training a perceptron

Modeling class probabilities via logistic regression

Logistic regression intuition and conditional probabilities

Learning the weights of the logistic cost function

Converting an Adaline implementation into an algorithm for logistic regression

Training a logistic regression model with scikit-learn

Tackling overfitting via regularization

Maximum margin classification with support vector machines

Maximum margin intuition

Dealing with a nonlinearly separable case using slack variables

Alternative implementations in scikit-learn

Solving nonlinear problems using a kernel SVM

Kernel methods for linearly inseparable data

Using the kernel trick to find separating hyperplanes in high-dimensional space

Decision tree learning

Maximizing information gain – getting the most bang for your buck

Building a decision tree

Combining multiple decision trees via random forests

K-nearest neighbors – a lazy learning algorithm

Summary

4. Building Good Training Sets – Data Preprocessing

Dealing with missing data

Identifying missing values in tabular data

Eliminating samples or features with missing values

Imputing missing values

Understanding the scikit-learn estimator API

Handling categorical data

Nominal and ordinal features

Creating an example dataset

Mapping ordinal features

Encoding class labels

Performing one-hot encoding on nominal features

Partitioning a dataset into separate training and test sets

Bringing features onto the same scale

Selecting meaningful features

L1 and L2 regularization as penalties against model complexity

A geometric interpretation of L2 regularization

Sparse solutions with L1 regularization

Sequential feature selection algorithms

Assessing feature importance with random forests

Summary

5. Compressing Data via Dimensionality Reduction

Unsupervised dimensionality reduction via principal component analysis

The main steps behind principal component analysis

Extracting the principal components step by step

Total and explained variance

Feature transformation

Principal component analysis in scikit-learn

Supervised data compression via linear discriminant analysis

Principal component analysis versus linear discriminant analysis

The inner workings of linear discriminant analysis

Computing the scatter matrices

Selecting linear discriminants for the new feature subspace

Projecting samples onto the new feature space

LDA via scikit-learn

Using kernel principal component analysis for nonlinear mappings

Kernel functions and the kernel trick

Implementing a kernel principal component analysis in Python

Example 1 – separating half-moon shapes

Example 2 – separating concentric circles

Projecting new data points

Kernel principal component analysis in scikit-learn

Summary

6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning

Streamlining workflows with pipelines

Loading the Breast Cancer Wisconsin dataset

Combining transformers and estimators in a pipeline

Using k-fold cross-validation to assess model performance

The holdout method

K-fold cross-validation

Debugging algorithms with learning and validation curves

Diagnosing bias and variance problems with learning curves

Addressing over- and underfitting with validation curves

Fine-tuning machine learning models via grid search

Tuning hyperparameters via grid search

Algorithm selection with nested cross-validation

Looking at different performance evaluation metrics

Reading a confusion matrix

Optimizing the precision and recall of a classification model

Plotting a receiver operating characteristic

Scoring metrics for multiclass classification

Dealing with class imbalance

Summary

7. Combining Different Models for Ensemble Learning

Learning with ensembles

Combining classifiers via majority vote

Implementing a simple majority vote classifier

Using the majority voting principle to make predictions

Evaluating and tuning the ensemble classifier

Bagging – building an ensemble of classifiers from bootstrap samples

Bagging in a nutshell

Applying bagging to classify samples in the Wine dataset

Leveraging weak learners via adaptive boosting

How boosting works

Applying AdaBoost using scikit-learn

Summary

8. Applying Machine Learning to Sentiment Analysis

Preparing the IMDb movie review data for text processing

Obtaining the movie review dataset

Preprocessing the movie dataset into more convenient format

Introducing the bag-of-words model

Transforming words into feature vectors

Assessing word relevancy via term frequency-inverse document frequency

Cleaning text data

Processing documents into tokens

Training a logistic regression model for document classification

Working with bigger data – online algorithms and out-of-core learning

Topic modeling with Latent Dirichlet Allocation

Decomposing text documents with LDA

LDA with scikit-learn

Summary

9. Embedding a Machine Learning Model into a Web Application

Serializing fitted scikit-learn estimators

Setting up an SQLite database for data storage

Developing a web application with Flask

Our first Flask web application

Form validation and rendering

Setting up the directory structure

Implementing a macro using the Jinja2 templating engine

Adding style via CSS

Creating the result page

Turning the movie review classifier into a web application

Files and folders – looking at the directory tree

Implementing the main application as app.py

Setting up the review form

Creating a results page template

Deploying the web application to a public server

Creating a PythonAnywhere account

Uploading the movie classifier application

Updating the movie classifier

Summary

10. Predicting Continuous Target Variables with Regression Analysis

Introducing linear regression

Simple linear regression

Multiple linear regression

Exploring the Housing dataset

Loading the Housing dataset into a data frame

Visualizing the important characteristics of a dataset

Looking at relationships using a correlation matrix

Implementing an ordinary least squares linear regression model

Solving regression for regression parameters with gradient descent

Estimating coefficient of a regression model via scikit-learn

Fitting a robust regression model using RANSAC

Evaluating the performance of linear regression models

Using regularized methods for regression

Turning a linear regression model into a curve – polynomial regression

Adding polynomial terms using scikit-learn

Modeling nonlinear relationships in the Housing dataset

Dealing with nonlinear relationships using random forests

Decision tree regression

Random forest regression

Summary

11. Working with Unlabeled Data – Clustering Analysis

Grouping objects by similarity using k-means

K-means clustering using scikit-learn

A smarter way of placing the initial cluster centroids using k-means++

Hard versus soft clustering

Using the elbow method to find the optimal number of clusters

Quantifying the quality of clustering via silhouette plots

Organizing clusters as a hierarchical tree

Grouping clusters in bottom-up fashion

Performing hierarchical clustering on a distance matrix

Attaching dendrograms to a heat map

Applying agglomerative clustering via scikit-learn

Locating regions of high density via DBSCAN

Summary

12. Implementing a Multilayer Artificial Neural Network from Scratch

Modeling complex functions with artificial neural networks

Single-layer neural network recap

Introducing the multilayer neural network architecture

Activating a neural network via forward propagation

Classifying handwritten digits

Obtaining the MNIST dataset

Implementing a multilayer perceptron

Training an artificial neural network

Computing the logistic cost function

Developing your intuition for backpropagation

Training neural networks via backpropagation

About the convergence in neural networks

A few last words about the neural network implementation

Summary

13. Parallelizing Neural Network Training with TensorFlow

TensorFlow and training performance

What is TensorFlow?

How we will learn TensorFlow

First steps with TensorFlow

Working with array structures

Developing a simple model with the low-level TensorFlow API

Training neural networks efficiently with high-level TensorFlow APIs

Building multilayer neural networks using TensorFlow's Layers API

Developing a multilayer neural network with Keras

Choosing activation functions for multilayer networks

Logistic function recap

Estimating class probabilities in multiclass classification via the softmax function

Broadening the output spectrum using a hyperbolic tangent

Rectified linear unit activation

Summary

14. Going Deeper – The Mechanics of TensorFlow

Key features of TensorFlow

TensorFlow ranks and tensors

How to get the rank and shape of a tensor

Understanding TensorFlow's computation graphs

Placeholders in TensorFlow

Defining placeholders

Feeding placeholders with data

Defining placeholders for data arrays with varying batchsizes

Variables in TensorFlow

Defining variables

Initializing variables

Variable scope

Reusing variables

Building a regression model

Executing objects in a TensorFlow graph using their names

Saving and restoring a model in TensorFlow

Transforming Tensors as multidimensional data arrays

Utilizing control flow mechanics in building graphs

Visualizing the graph with TensorBoard

Extending your TensorBoard experience

Summary

15. Classifying Images with Deep Convolutional Neural Networks

Building blocks of convolutional neural networks

Understanding CNNs and learning feature hierarchies

Performing discrete convolutions

Performing a discrete convolution in one dimension

The effect of zero-padding in a convolution

Determining the size of the convolution output

Performing a discrete convolution in 2D

Subsampling

Putting everything together to build a CNN

Working with multiple input or color channels

Regularizing a neural network with dropout

Implementing a deep convolutional neural network using TensorFlow

The multilayer CNN architecture

Loading and preprocessing the data

Implementing a CNN in the TensorFlow low-level API

Implementing a CNN in the TensorFlow Layers API

Summary

16. Modeling Sequential Data Using Recurrent Neural Networks

Introducing sequential data

Modeling sequential data – order matters

Representing sequences

The different categories of sequence modeling

RNNs for modeling sequences

Understanding the structure and flow of an RNN

Computing activations in an RNN

The challenges of learning long-range interactions

LSTM units

Implementing a multilayer RNN for sequence modeling in TensorFlow

Project one – performing sentiment analysis of IMDb movie reviews using multilayer RNNs

Preparing the data

Embedding

Building an RNN model

The SentimentRNN class constructor

The build method

Step 1 – defining multilayer RNN cells

Step 2 – defining the initial states for the RNN cells

Step 3 – creating the RNN using the RNN cells and their states

The train method

The predict method

Instantiating the SentimentRNN class

Training and optimizing the sentiment analysis RNN model

Project two – implementing an RNN for character-level language modeling in TensorFlow

Preparing the data

Building a character-level RNN model

The constructor

The build method

The train method

The sample method

Creating and training the CharRNN Model

The CharRNN model in the sampling mode

Chapter and book summary

Index

Python Machine Learning Second Edition

Python Machine Learning Second Edition

Copyright © 2017 Packt Publishing

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First published: September 2015

Second edition: September 2017

Revision log for the second edition:

First release: 14-09-2017

Second release: 15-09-2017

Third release: 23-10-2017

Fourth release: 04-09-2018

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Credits

Authors

Sebastian Raschka

Vahid Mirjalili

Reviewers

Jared Huffman

Huai-En, Sun (Ryan Sun)

Acquisition Editor

Frank Pohlmann

Content Development Editor

Chris Nelson

Project Editor

Monika Sangwan

Technical Editors

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Nidhisha Shetty

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Indexer

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Graphics

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About the Authors

Sebastian Raschka, the author of the bestselling book, Python Machine Learning, has many years of experience with coding in Python, and he has given several seminars on the practical applications of data science, machine learning, and deep learning including a machine learning tutorial at SciPy—the leading conference for scientific computing in Python.

While Sebastian's academic research projects are mainly centered around problem-solving in computational biology, he loves to write and talk about data science, machine learning, and Python in general, and he is motivated to help people develop data-driven solutions without necessarily requiring a machine learning background.

His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017 as well as the ACM Computing Reviews’ Best of 2016 award. In his free time, Sebastian loves to contribute to open source projects, and the methods that he has implemented are now successfully used in machine learning competitions, such as Kaggle.

I would like to take this opportunity to thank the great Python community and developers of open source packages who helped me create the perfect environment for scientific research and data science. Also, I want to thank my parents who always encouraged and supported me in pursuing the path and career that I was so passionate about.

Special thanks to the core developers of scikit-learn. As a contributor to this project, I had the pleasure to work with great people who are not only very knowledgeable when it comes to machine learning but are also excellent programmers.

Lastly, I'd like to thank Elie Kawerk, who volunteered to review the book and provided valuable feedback on the new chapters.

Vahid Mirjalili obtained his PhD in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Currently, he is focusing his research efforts on applications of machine learning in various computer vision projects at the department of computer science and engineering at Michigan State University.

Vahid picked Python as his number-one choice of programming language, and throughout his academic and research career he has gained tremendous experience with coding in Python. He taught Python programming to the engineering class at Michigan State University, which gave him a chance to help students understand different data structures and develop efficient code in Python.

While Vahid's broad research interests focus on deep learning and computer vision applications, he is especially interested in leveraging deep learning techniques to extend privacy in biometric data such as face images so that information is not revealed beyond what users intend to reveal. Furthermore, he also collaborates with a team of engineers working on self-driving cars, where he designs neural network models for the fusion of multispectral images for pedestrian detection.

I would like to thank my PhD advisor, Dr. Arun Ross, for giving me the opportunity to work on novel problems in his research lab. I also like to thank Dr. Vishnu Boddeti for inspiring my interests in deep learning and demystifying its core concepts.

About the Reviewers

Jared Huffman is an entrepreneur, gamer, storyteller, machine learning fanatic, and database aficionado. He has dedicated the past 10 years to developing software and analyzing data. His previous work has spanned a variety of topics, including network security, financial systems, and business intelligence, as well as web services, developer tools, and business strategy. Most recently, he was the founder of the data science team at Minecraft, with a focus on big data and machine learning. When not working, you can typically find him gaming or enjoying the beautiful Pacific Northwest with friends and family.

I'd like to thank Packt for giving me the opportunity to work on such a great book, my wife for the constant encouragement, and my daughter for sleeping through most of the late nights while I was reviewing and debugging code.

Huai-En, Sun (Ryan Sun) holds a master's degree in statistics from the National Chiao Tung University. He is currently working as a data scientist for analyzing the production line at PEGATRON. Machine learning and deep learning are his main areas of research.

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Preface

Through exposure to the news and social media, you are probably aware of the fact that machine learning has become one of the most exciting technologies of our time and age. Large companies, such as Google, Facebook, Apple, Amazon, and IBM, heavily invest in machine learning research and applications for good reasons. While it may seem that machine learning has become the buzzword of our time and age, it is certainly not a fad. This exciting field opens the way to new possibilities and has become indispensable to our daily lives. This is evident in talking to the voice assistant on our smartphones, recommending the right product for our customers, preventing credit card fraud, filtering out spam from our email 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. Many practical books have been published in recent years that will help you get started in machine learning by implementing powerful learning algorithms.

Getting exposed to practical code examples and working through example applications of machine learning are a great way to dive into this field. Concrete examples help illustrate the broader concepts by putting the learned material directly into action. However, remember that with great power comes great responsibility! In addition to offering a hands-on experience with machine learning using the Python programming languages and Python-based machine learning libraries, this book introduces the mathematical concepts behind machine learning algorithms, which is essential for using machine learning successfully. Thus, this book is different from a purely practical book; it is a book that discusses the necessary details regarding machine learning concepts and offers intuitive yet informative explanations of how machine learning algorithms work, how to use them, and most importantly, how to avoid the most common pitfalls.

Currently, if you type "machine learning" as a search term in Google Scholar, it returns an overwhelmingly large number of publications—1,800,000. Of course, we cannot discuss the nitty-gritty of 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 field. If you find that your thirst for knowledge is not satisfied, this book references many useful resources that can be used to follow up on the essential breakthroughs in this field.

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 actually works, this book is for you. Don't worry if you are completely new to the machine learning field; you have even more reason to be excited. Here is a promise 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 field, let's 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.

We, the authors, can truly say that the study of machine learning has made us better scientists, thinkers, and problem solvers. In this book, we want to share this knowledge with you. Knowledge is gained by learning. The key is our enthusiasm, and the real 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 we 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 in order to tackle various problem tasks. In addition, it discusses the essential steps for creating a typical machine learning model by building a pipeline that will guide us through the following chapters.

Chapter 2, Training Simple Machine Learning Algorithms for Classification, goes back to the origins of machine learning and introduces binary perceptron classifiers and adaptive linear neurons. This chapter is a gentle introduction to the fundamentals of pattern classification and focuses on the interplay of optimization algorithms and machine learning.

Chapter 3, A Tour of Machine Learning Classifiers Using scikit-learn, describes the essential machine learning algorithms for classification and provides practical examples using one of the most popular and comprehensive open source machine learning libraries: scikit-learn.

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 input 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 dos 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 fine-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 find groups of objects that share a certain degree of similarity.

Chapter 12, Implementing a Multilayer Artificial Neural Network from Scratch, extends the concept of gradient-based optimization, which we first introduced in Chapter 2, Training Simple Machine Learning Algorithms for Classification, to build powerful, multilayer neural networks based on the popular backpropagation algorithm in Python.

Chapter 13, Parallelizing Neural Network Training with TensorFlow, builds upon the knowledge from the previous chapter to provide you with a practical guide for training neural networks more efficiently. The focus of this chapter is on TensorFlow, an open source Python library that allows us to utilize multiple cores of modern GPUs.

Chapter 14, Going Deeper – The Mechanics of TensorFlow, covers TensorFlow in greater detail explaining its core concepts of computational graphs and sessions. In addition, this chapter covers topics such as saving and visualizing neural network graphs, which will come in very handy during the remaining chapters of this book.

Chapter 15, Classifying Images with Deep Convolutional Neural Networks, discusses deep neural network architectures that have become the new standard in computer vision and image recognition fields—convolutional neural networks. This chapter will discuss the main concepts between convolutional layers as a feature extractor and apply convolutional neural network architectures to an image classification task to achieve almost perfect classification accuracy.

Chapter 16, Modeling Sequential Data Using Recurrent Neural Networks, introduces another popular neural network architecture for deep learning that is especially well suited for working with sequential data and time series data. In this chapter, we will apply different recurrent neural network architectures to text data. We will start with a sentiment analysis task as a warm-up exercise and will learn how to generate entirely new text.

What you need for this book

The execution of the code examples provided in this book requires an installation of Python 3.6.0 or newer on macOS, Linux, or Microsoft Windows. We will make frequent use of Python's essential libraries for scientific computing throughout this book, including SciPy, NumPy, scikit-learn, Matplotlib, and pandas.

The first chapter will provide you with instructions and useful tips to set up your Python environment and these core libraries. We will add additional libraries to our repertoire; moreover, installation instructions are provided in the respective chapters: the NLTK library for natural language processing (Chapter 8, Applying Machine Learning to Sentiment Analysis), the Flask web framework (Chapter 9, Embedding a Machine Learning Algorithm into a Web Application), the Seaborn library for statistical data visualization (Chapter 10, Predicting Continuous Target Variables with Regression Analysis), and TensorFlow for efficient neural network training on graphical processing units (Chapters 13 to 16).

Who this book is for

If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning, Second Edition—whether you want to start from scratch or extend your data science knowledge, this is an essential and unmissable resource.

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Chapter 1. Giving Computers the Ability to Learn from Data

In my opinion, machine learning, the application and science of algorithms that make sense of data, is the most exciting field of all the computer sciences! We are living in an age where data comes in abundance; using self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Thanks to the many powerful open source libraries that have been developed in recent years, there has probably never been a better time to break into the machine learning field and learn how to utilize powerful algorithms to spot patterns in data and make predictions about future events.

In this chapter, you will learn about the main concepts and different types of machine learning. Together with a basic introduction to the relevant terminology, we will lay the groundwork for successfully using machine learning techniques for practical problem solving.

In this chapter, we will cover the following topics:

The general concepts of machine learningThe three types of learning and basic terminologyThe building blocks for successfully designing machine learning systemsInstalling and setting up Python for data analysis and machine learning

Building intelligent machines to transform data into knowledge

In this age of modern technology, there is one resource that we have in abundance: a large amount of structured and unstructured data. In the second half of the twentieth century, machine learning evolved as a subfield of Artificial Intelligence (AI) that involved self-learning algorithms that derived knowledge from data in order to make predictions. Instead of requiring humans to manually derive rules and build models from analyzing large amounts of data, machine learning offers a more efficient alternative for capturing the knowledge in data to gradually improve the performance of predictive models and make data-driven decisions. Not only is machine learning becoming increasingly important in computer science research, but it also plays an ever greater role in our everyday lives. Thanks to machine learning, we enjoy robust email spam filters, convenient text and voice recognition software, reliable web search engines, challenging chess-playing programs, and, hopefully soon, safe and efficient self-driving cars.

The three different types of machine learning

In this section, we will take a look at the three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. We will learn about the fundamental differences between the three different learning types and, using conceptual examples, we will develop an intuition for the practical problem domains where these can be applied:

Making predictions about the future with supervised learning

The main goal in supervised learning is to learn a model from labeled training data that allows us to make predictions about unseen or future data. Here, the term supervised refers to a set of samples where the desired output signals (labels) are already known.

Considering the example of email spam filtering, we can train a model using a supervised machine learning algorithm on a corpus of labeled emails, emails that are correctly marked as spam or not-spam, to predict whether a new email belongs to either of the two categories. A supervised learning task with discrete class labels, such as in the previous email spam filtering example, is also called a classification task. Another subcategory of supervised learning is regression, where the outcome signal is a continuous value:

Classification for predicting class labels

Classification is a subcategory of supervised learning where the goal is to predict the categorical class labels of new instances, based on past observations. Those class labels are discrete, unordered values that can be understood as the group memberships of the instances. The previously mentioned example of email spam detection represents a typical example of a binary classification task, where the machine learning algorithm learns a set of rules in order to distinguish between two possible classes: spam and non-spam emails.

However, the set of class labels does not have to be of a binary nature. The predictive model learned by a supervised learning algorithm can assign any class label that was presented in the training dataset to a new, unlabeled instance. A typical example of a multiclass classification task is handwritten character recognition. Here, we could collect a training dataset that consists of multiple handwritten examples of each letter in the alphabet. Now, if a user provides a new handwritten character via an input device, our predictive model will be able to predict the correct letter in the alphabet with certain accuracy. However, our machine learning system would be unable to correctly recognize any of the digits zero to nine, for example, if they were not part of our training dataset.

The following figure illustrates the concept of a binary classification task given 30 training samples; 15 training samples are labeled as negative class (minus signs) and 15 training samples are labeled as positive class (plus signs). In this scenario, our dataset is two-dimensional, which means that each sample has two values associated with it: and . Now, we can use a supervised machine learning algorithm to learn a rule—the decision boundary represented as a dashed line—that can separate those two classes and classify new data into each of those two categories given its and values:

Regression for predicting continuous outcomes

We learned in the previous section that the task of classification is to assign categorical, unordered labels to instances. A second type of supervised learning is the prediction of continuous outcomes, which is also called regression analysis. In regression analysis, we are given a number of predictor (explanatory) variables and a continuous response variable (outcome or target), and we try to find a relationship between those variables that allows us to predict an outcome.

For example, let's assume that we are interested in predicting the math SAT scores of our students. If there is a relationship between the time spent studying for the test and the final scores, we could use it as training data to learn a model that uses the study time to predict the test scores of future students who are planning to take this test.

Note

The term regression was devised by Francis Galton in his article Regression towards Mediocrity in Hereditary Stature in 1886. Galton described the biological phenomenon that the variance of height in a population does not increase over time. He observed that the height of parents is not passed on to their children, but instead the children's height is regressing towards the population mean.

The following figure illustrates the concept of linear regression. Given a predictor variable x and a response variable y, we fit a straight line to this data that minimizes the distance—most commonly the average squared distance—between the sample points and the fitted line. We can now use the intercept and slope learned from this data to predict the outcome variable of new data:

Solving interactive problems with reinforcement learning

Another type of machine learning is reinforcement learning. In reinforcement learning, the goal is to develop a system (agent) that improves its performance based on interactions with the environment. Since the information about the current state of the environment typically also includes a so-called reward signal, we can think of reinforcement learning as a field related to supervised learning. However, in reinforcement learning this feedback is not the correct ground truth label or value, but a measure of how well the action was measured by a reward function. Through its interaction with the environment, an agent can then use reinforcement learning to learn a series of actions that maximizes this reward via an exploratory trial-and-error approach or deliberative planning.

A popular example of reinforcement learning is a chess engine. Here, the agent decides upon a series of moves depending on the state of the board (the environment), and the reward can be defined as win or lose at the end of the game:

There are many different subtypes of reinforcement learning. However, a general scheme is that the agent in reinforcement learning tries to maximize the reward by a series of interactions with the environment. Each state can be associated with a positive or negative reward, and a reward can be defined as accomplishing an overall goal, such as winning or losing a game of chess. For instance, in chess the outcome of each move can be thought of as a different state of the environment. To explore the chess example further, let's think of visiting certain locations on the chess board as being associated with a positive event—for instance, removing an opponent's chess piece from the board or threatening the queen. Other positions, however, are associated with a negative event, such as losing a chess piece to the opponent in the following turn. Now, not every turn results in the removal of a chess piece, and reinforcement learning is concerned with learning the series of steps by maximizing a reward based on immediate and delayed feedback.

While this section provides a basic overview of reinforcement learning, please note that applications of reinforcement learning are beyond the scope of this book, which primarily focusses on classification, regression analysis, and clustering.

Discovering hidden structures with unsupervised learning

In supervised learning, we know the right answer beforehand when we train our model, and in reinforcement learning, we define a measure of reward for particular actions by the agent. In unsupervised learning, however, we are dealing with unlabeled data or data of unknown structure. Using unsupervised learning techniques, we are able to explore the structure of our data to extract meaningful information without the guidance of a known outcome variable or reward function.

Finding subgroups with clustering

Clustering is an exploratory data analysis technique that allows us to organize a pile of information into meaningful subgroups (clusters) without having any prior knowledge of their group memberships. Each cluster that arises during the analysis defines a group of objects that share a certain degree of similarity but are more dissimilar to objects in other clusters, which is why clustering is also sometimes called unsupervised classification. Clustering is a great technique for structuring information and deriving meaningful relationships from data. For example, it allows marketers to discover customer groups based on their interests, in order to develop distinct marketing programs.

The following figure illustrates how clustering can be applied to organizing unlabeled data into three distinct groups based on the similarity of their features and :

Dimensionality reduction for data compression

Another subfield of unsupervised learning is dimensionality reduction. Often we are working with data of high dimensionality—each observation comes with a high number of measurements—that can present a challenge for limited storage space and the computational performance of machine learning algorithms. Unsupervised dimensionality reduction is a commonly used approach in feature preprocessing to remove noise from data, which can also degrade the predictive performance of certain algorithms, and compress the data onto a smaller dimensional subspace while retaining most of the relevant information.

Sometimes, dimensionality reduction can also be useful for visualizing data, for example, a high-dimensional feature set can be projected onto one-, two-, or three-dimensional feature spaces in order to visualize it via 3D or 2D scatterplots or histograms. The following figure shows an example where nonlinear dimensionality reduction was applied to compress a 3D Swiss Roll onto a new 2D feature subspace:

Introduction to the basic terminology and notations

Now that we have discussed the three broad categories of machine learning—supervised, unsupervised, and reinforcement learning—let us have a look at the basic terminology that we will be using throughout the book. The following table depicts an excerpt of the Iris dataset, which is a classic example in the field of machine learning. The Iris dataset contains the measurements of 150 Iris flowers from three different species—Setosa, Versicolor, and Virginica. Here, each flower sample represents one row in our dataset, and the flower measurements in centimeters are stored as columns, which we also call the features of the dataset:

To keep the notation and implementation simple yet efficient, we will make use of some of the basics of linear algebra. In the following chapters, we will use a matrix and vector notation to refer to our data. We will follow the common convention to represent each sample as a separate row in a feature matrix X, where each feature is stored as a separate column.

The Iris dataset consisting of 150 samples and four features can then be written as a matrix :

Note

For the rest of this book, unless noted otherwise, we will use the superscript i to refer to the ith training sample, and the subscript j to refer to the jth dimension of the training dataset.

We use lowercase, bold-face letters to refer to vectors and uppercase, bold-face letters to refer to matrices . To refer to single elements in a vector or matrix, we write the letters in italics ( or , respectively).

For example, refers to the first dimension of flower sample 150, the sepal length. Thus, each row in this feature matrix represents one flower instance and can be written as a four-dimensional row vector :

And each feature dimension is a 150-dimensional column vector . For example:

Similarly, we store the target variables (here, class labels) as a 150-dimensional column vector:

A roadmap for building machine learning systems

In previous sections, we discussed the basic concepts of machine learning and the three different types of learning. In this section, we will discuss the other important parts of a machine learning system accompanying the learning algorithm. The following diagram shows a typical workflow for using machine learning in predictive modeling, which we will discuss in the following subsections:

Preprocessing – getting data into shape

Let's begin with discussing the roadmap for building machine learning systems. Raw data rarely comes in the form and shape that is necessary for the optimal performance of a learning algorithm. Thus, the preprocessing of the data is one of the most crucial steps in any machine learning application. If we take the Iris flower dataset from the previous section as an example, we can think of the raw data as a series of flower images from which we want to extract meaningful features. Useful features could be the color, the hue, the intensity of the flowers, the height, and the flower lengths and widths. Many machine learning algorithms also require that the selected features are on the same scale for optimal performance, which is often achieved by transforming the features in the range [0, 1] or a standard normal distribution with zero mean and unit variance, as we will see in later chapters.

Some of the selected features may be highly correlated and therefore redundant to a certain degree. In those cases, dimensionality reduction techniques are useful for compressing the features onto a lower dimensional subspace. Reducing the dimensionality of our feature space has the advantage that less storage space is required, and the learning algorithm can run much faster. In certain cases, dimensionality reduction can also improve the predictive performance of a model if the dataset contains a large number of irrelevant features (or noise), that is, if the dataset has a low signal-to-noise ratio.

To determine whether our machine learning algorithm not only performs well on the training set but also generalizes well to new data, we also want to randomly divide the dataset into a separate training and test set. We use the training set to train and optimize our machine learning model, while we keep the test set until the very end to evaluate the final model.

Training and selecting a predictive model

As we will see in later chapters, many different machine learning algorithms have been developed to solve different problem tasks. An important point that can be summarized from David Wolpert's famous No free lunch theorems is that we can't get learning "for free" (The Lack of A Priori Distinctions Between Learning Algorithms, D.H. Wolpert 1996; No free lunch theorems for optimization, D.H. Wolpert and W.G. Macready, 1997). Intuitively, we can relate this concept to the popular saying, I suppose it is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail (Abraham Maslow, 1966). For example, each classification algorithm has its inherent biases, and no single classification model enjoys superiority if we don't make any assumptions about the task. In practice, it is therefore essential to compare at least a handful of different algorithms in order to train and select the best performing model. But before we can compare different models, we first have to decide upon a metric to measure performance. One commonly used metric is classification accuracy, which is defined as the proportion of correctly classified instances.

One legitimate question to ask is this: how do we know which model performs well on the final test dataset and real-world data if we don't use this test set for the model selection, but keep it for the final model evaluation? In order to address the issue embedded in this question, different cross-validation techniques can be used where the training dataset is further divided into training and validation subsets in order to estimate the generalization performance of the model. Finally, we also cannot expect that the default parameters of the different learning algorithms provided by software libraries are optimal for our specific problem task. Therefore, we will make frequent use of hyperparameter optimization techniques that help us to fine-tune the performance of our model in later chapters. Intuitively, we can think of those hyperparameters as parameters that are not learned from the data but represent the knobs of a model that we can turn to improve its performance. This will become much clearer in later chapters when we see actual examples.

Evaluating models and predicting unseen data instances

After we have selected a model that has been fitted on the training dataset, we can use the test dataset to estimate how well it performs on this unseen data to estimate the generalization error. If we are satisfied with its performance, we can now use this model to predict new, future data. It is important to note that the parameters for the previously mentioned procedures, such as feature scaling and dimensionality reduction, are solely obtained from the training dataset, and the same parameters are later reapplied to transform the test dataset, as well as any new data samples—the performance measured on the test data may be overly optimistic otherwise.

Using Python for machine learning

Python is one of the most popular programming languages for data science and therefore enjoys a large number of useful add-on libraries developed by its great developer and open-source community.

Although the performance of interpreted languages, such as Python, for computation-intensive tasks is inferior to lower-level programming languages, extension libraries such as NumPy and SciPy have been developed that build upon lower-layer Fortran and C implementations for fast and vectorized operations on multidimensional arrays.

For machine learning programming tasks, we will mostly refer to the scikit-learn library, which is currently one of the most popular and accessible open source machine learning libraries.

Installing Python and packages from the Python Package Index

Python is available for all three major operating systems—Microsoft Windows, macOS, and Linux—and the installer, as well as the documentation, can be downloaded from the official Python website: https://www.python.org.

This book is written for Python version 3.5.2 or higher, and it is recommended you use the most recent version of Python 3 that is currently available, although most of the code examples may also be compatible with Python 2.7.13 or higher. If you decide to use Python 2.7 to execute the code examples, please make sure that you know about the major differences between the two Python versions. A good summary of the differences between Python 3.5 and 2.7 can be found at https://wiki.python.org/moin/Python2orPython3.

The additional packages that we will be using throughout this book can be installed via the pip installer program, which has been part of the Python standard library since Python 3.3. More information about pip can be found at https://docs.python.org/3/installing/index.html.

After we have successfully installed Python, we can execute pip from the Terminal to install additional Python packages:

pip install SomePackage

Already installed packages can be updated via the --upgrade flag:

pip install SomePackage --upgrade

Using the Anaconda Python distribution and package manager

A highly recommended alternative Python distribution for scientific computing is Anaconda by Continuum Analytics. Anaconda is a free—including for commercial use—enterprise-ready Python distribution that bundles all the essential Python packages for data science, math, and engineering in one user-friendly cross-platform distribution. The Anaconda installer can be downloaded at http://continuum.io/downloads, and an Anaconda quick-start guide is available at https://conda.io/docs/test-drive.html.

After successfully installing Anaconda, we can install new Python packages using the following command:

conda install SomePackage

Existing packages can be updated using the following command:

conda update SomePackage

Packages for scientific computing, data science, and machine learning

Throughout this book, we will mainly use NumPy's multidimensional arrays to store and manipulate data. Occasionally, we will make use of pandas, which is a library built on top of NumPy that provides additional higher-level data manipulation tools that make working with tabular data even more convenient. To augment our learning experience and visualize quantitative data, which is often extremely useful to intuitively make sense of it, we will use the very customizable Matplotlib library.

The version numbers of the major Python packages that were used for writing this book are mentioned in the following list. Please make sure that the version numbers of your installed packages are equal to, or greater than, those version numbers to ensure the code examples run correctly:

NumPy 1.12.1SciPy 0.19.0scikit-learn 0.18.1Matplotlib 2.0.2pandas 0.20.1

Summary

In this chapter, we explored machine learning at a very high level and familiarized ourselves with the big picture and major concepts that we are going to explore in the following chapters in more detail. We learned that supervised learning is composed of two important subfields: classification and regression. While classification models allow us to categorize objects into known classes, we can use regression analysis to predict the continuous outcomes of target variables. Unsupervised learning not only offers useful techniques for discovering structures in unlabeled data, but it can also be useful for data compression in feature preprocessing steps. We briefly went over the typical roadmap for applying machine learning to problem tasks, which we will use as a foundation for deeper discussions and hands-on examples in the following chapters. Eventually, we set up our Python environment and installed and updated the required packages to get ready to see machine learning in action.

Later in this book, in addition to machine learning itself, we will also introduce different techniques to preprocess our dataset, which will help us to get the best performance out of different machine learning algorithms. While we will cover classification algorithms quite extensively throughout the book, we will also explore different techniques for regression analysis and clustering.

We have an exciting journey ahead, covering many powerful techniques in the vast field of machine learning. However, we will approach machine learning one step at a time, building upon our knowledge gradually throughout the chapters of this book. In the following chapter, we will start this journey by implementing one of the earliest machine learning algorithms for classification, which will prepare us for Chapter 3, A Tour of Machine Learning Classifiers Using scikit-learn, where we cover more advanced machine learning algorithms using the scikit-learn open source machine learning library.

Chapter 2. Training Simple Machine Learning Algorithms for Classification

In this chapter, we will make use of two of the first algorithmically described machine learning algorithms for classification, the perceptron and adaptive linear neurons. We will start by implementing a perceptron step by step in Python and training it to classify different flower species in the Iris dataset. This will help us understand the concept of machine learning algorithms for classification and how they can be efficiently implemented in Python.

Discussing the basics of optimization using adaptive linear neurons will then lay the groundwork for using more powerful classifiers via the scikit-learn machine learning library in Chapter 3, A Tour of Machine Learning Classifiers Using scikit-learn.

The topics that we will cover in this chapter are as follows:

Building an intuition for machine learning algorithmsUsing pandas, NumPy, and Matplotlib to read in, process, and visualize dataImplementing linear classification algorithms in Python

Artificial neurons – a brief glimpse into the early history of machine learning

Before we discuss the perceptron and related algorithms in more detail, let us take a brief tour through the early beginnings of machine learning. Trying to understand how the biological brain works, in order to design AI, Warren McCulloch and Walter Pitts published the first concept of a simplified brain cell, the so-called McCulloch-Pitts (MCP) neuron, in 1943 (A Logical Calculus of the Ideas Immanent in Nervous Activity, W. S. McCulloch and W. Pitts, Bulletin of Mathematical Biophysics, 5(4): 115-133, 1943). Neurons are interconnected nerve cells in the brain that are involved in the processing and transmitting of chemical and electrical signals, which is illustrated in the following figure:

McCulloch and Pitts described such a nerve cell as a simple logic gate with binary outputs; multiple signals arrive at the dendrites, are then integrated into the cell body, and, if the accumulated signal exceeds a certain threshold, an output signal is generated that will be passed on by the axon.

Only a few years later, Frank Rosenblatt published the first concept of the perceptron learning rule based on the MCP neuron model (The Perceptron: A Perceiving and Recognizing Automaton, F. Rosenblatt, Cornell Aeronautical Laboratory, 1957