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Python Machine Learning, Second Edition

Python Machine Learning, Second Edition

By : Sebastian Raschka, Vahid Mirjalili
4.3 (89)
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Python Machine Learning, Second Edition

Python Machine Learning, Second Edition

4.3 (89)
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Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries.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)
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17
Index

Conventions

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."

Note

Warnings or important notes appear in a box like this.

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