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Python Machine Learning

You're reading from   Python Machine Learning Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789955750
Length 772 pages
Edition 3rd Edition
Languages
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Authors (2):
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Vahid Mirjalili Vahid Mirjalili
Author Profile Icon Vahid Mirjalili
Vahid Mirjalili
Sebastian Raschka Sebastian Raschka
Author Profile Icon Sebastian Raschka
Sebastian Raschka
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Toc

Table of Contents (21) Chapters Close

Preface 1. Giving Computers the Ability to Learn from Data FREE CHAPTER 2. Training Simple Machine Learning Algorithms for Classification 3. A Tour of Machine Learning Classifiers Using scikit-learn 4. Building Good Training Datasets – Data Preprocessing 5. Compressing Data via Dimensionality Reduction 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning 7. Combining Different Models for Ensemble Learning 8. Applying Machine Learning to Sentiment Analysis 9. Embedding a Machine Learning Model into a Web Application 10. Predicting Continuous Target Variables with Regression Analysis 11. Working with Unlabeled Data – Clustering Analysis 12. Implementing a Multilayer Artificial Neural Network from Scratch 13. Parallelizing Neural Network Training with TensorFlow 14. Going Deeper – The Mechanics of TensorFlow 15. Classifying Images with Deep Convolutional Neural Networks 16. Modeling Sequential Data Using Recurrent Neural Networks 17. Generative Adversarial Networks for Synthesizing New Data 18. Reinforcement Learning for Decision Making in Complex Environments 19. Other Books You May Enjoy 20. Index

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All code in this book is also available in the form of Jupyter notebooks, and a short introduction can be found in the code directory of Chapter 1, Giving Computers the Ability to Learn from Data, at https://github.com/rasbt/python-machine-learning-book-3rd-edition/tree/master/ch01#pythonjupyter-notebook. For more information about the general Jupyter Notebook GUI, please see the official documentation at https://jupyter-notebook.readthedocs.io/en/stable/.

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Code words in text are shown as follows: "And already installed packages can be updated via the --upgrade flag."

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>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> y = df.iloc[0:100, 4].values
>>> y = np.where(y == 'Iris-setosa', -1, 1)
>>> X = df.iloc[0:100, [0, 2]].values
>>> plt.scatter(X[:50, 0], X[:50, 1],
...             color='red', marker='x', label='setosa')
>>> plt.scatter(X[50:100, 0], X[50:100, 1],
...             color='blue', marker='o', label='versicolor')
>>> plt.xlabel('sepal length')
>>> plt.ylabel('petal length')
>>> plt.legend(loc='upper left')
>>> plt.show()

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