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.

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).

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.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Using the out_file=None setting, we directly assigned the dot data to a dot_data variable, instead of writing an intermediate tree.dot file to disk."

A block of code is set as follows:

>>> from sklearn.neighbors import KNeighborsClassifier
>>> knn = KNeighborsClassifier(n_neighbors=5, p=2,
...                            metric='minkowski')
>>> knn.fit(X_train_std, y_train)
>>> plot_decision_regions(X_combined_std, y_combined, 
...                       classifier=knn, test_idx=range(105,150))
>>> plt.xlabel('petal length [standardized]')
>>> plt.ylabel('petal width [standardized]')
>>> plt.show()

Any command-line input or output is written as follows:

pip3 install graphviz

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "After we click on the Dashboard button in the top-right corner, we have access to the control panel shown at the top of the page."

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