Machine Learning on a Cancer Dataset - Part 24

This is the 24th tutorial on machine learning with scikit-learn and the last one for neural networks.

Here we review the strong and the weak points of this algorithm with respect to scikit-learn. We also talk about alternatives when it comes to neural networks and machine learning, and most specifically, deep learning. You may be familiar with or have heard of tensorflow, theano, lasagna, keras, and the like. Please watch the video for the full scoop. Next, we're gonna start dealing with support vector machines (SVMs).

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As a reminder:

In this series I'm going to explore the cancer dataset that comes pre-loaded with scikit-learn. The purpose is to train the classifiers on this dataset, which consists of labeled data: ~569 tumor samples, each labeled malignant or benign, and then use them on new, unlabeled data.

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Previous videos in this series:

1. Machine Learning on a Cancer Dataset - Part 20
2. Machine Learning on a Cancer Dataset - Part 21
3. Machine Learning on a Cancer Dataset - Part 22
4. Machine Learning on a Cancer Dataset - Part 23

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To stay in touch with me, follow @cristi

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Cristi Vlad, Self-Experimenter and Author