Book Image

Python Machine Learning, Second Edition - Second Edition

By : Sebastian Raschka, Vahid Mirjalili
Book Image

Python Machine Learning, Second Edition - Second Edition

By: Sebastian Raschka, Vahid Mirjalili

Overview of this book

Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries.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.
Table of Contents (18 chapters)
17
Index

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:

Introduction to the basic terminology and notations

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 Introduction to the basic terminology and notations matrix Introduction to the basic terminology and notations:

Introduction to the basic terminology and notations

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 Introduction to the basic terminology and notations and uppercase, bold-face letters to refer to matrices Introduction to the basic terminology and notations. To refer to single elements in a vector or matrix, we write the letters in italics (Introduction to the basic terminology and notations or Introduction to the basic terminology and notations , respectively).

For example, Introduction to the basic terminology and notations 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 Introduction to the basic terminology and notations:

Introduction to the basic terminology and notations

And each feature dimension is a 150-dimensional column vector Introduction to the basic terminology and notations. For example:

Introduction to the basic terminology and notations

Similarly, we store the target variables (here, class labels) as a 150-dimensional column vector: Introduction to the basic terminology and notations