In this chapter, we gained a good understanding of the basic concepts of linear classifiers for supervised learning. After we implemented a perceptron, we saw how we can train adaptive linear neurons efficiently via a vectorized implementation of gradient descent and on-line learning via stochastic gradient descent. Now that we have seen how to implement simple classifiers in Python, we are ready to move on to the next chapter where we will use the Python scikit-learn machine learning library to get access to more advanced and powerful off-the-shelf machine learning classifiers that are commonly used in academia as well as in industry.
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You're reading from Python Machine Learning
Product typeBook
Published inSep 2015
Reading LevelIntermediate
PublisherPackt
ISBN-139781783555130
Edition1st Edition
Languages
Concepts
Author (1)
Sebastian Raschka
Sebastian Raschka
Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.
Read more about Sebastian Raschka
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Python Machine LearningPublished in: Sep 2015Publisher: PacktISBN-13: 9781783555130
© 2015 Packt Publishing Limited All Rights Reserved
Author (1)
Sebastian Raschka
Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.
Read more about Sebastian Raschka