Machine Learning on a Cancer Dataset - Part 4
Before getting into training specific algorithms on this dataset, I want to briefly go over the machine learning process. This is often applied in many ML projects, so it's kind of nice to review it. In short, many ML projects occur in the following sequence:
- Get the data. Preprocess it, if necessary.
- Pick an algorithm or classifier to use.
- Train the algorith. Check its accuracy and optimize, if needed.
- Test the algorithm on new data.
More details in the video...
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.
Previous videos in this series:
- Machine Learning on a Cancer Dataset - Part 1
- Machine Learning on a Cancer Dataset - Part 2
- Machine Learning on a Cancer Dataset - Part 3
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Cristi Vlad, Self-Experimenter and Author
Mate you are like a machine with the amount of stuff you manage to create! I remember you were also doing some workout and nutrition type stuff before too which was very interesting - do you have any plans to continue that?
https://twitter.com/Soul_Eater_43/status/843490492054691842
hey thanks for the feedback. I'm not sure what part of nutrition you refer to. I have written 4 books related to that, 3 of them are longform (150 - 220 pages) and one is a shorter one 80-90 pages. I am studying biochem and I still post videos from Lehningers biochem textbook, but that is currently (for the next few days) on hold. I've finished a video series on fatty acid catabolism and I'm figuring out what to go about next: I'm going through bioenergetics, which is a tough topic (and often controversial as it has powerful implications with physics, but Idk...not sure if this answers your question though
That is probably what I was thinking about - your books and other videos!
#ai
Disclaimer: I am just a bot trying to be helpful.
This should be supported.
Interesting stuff. This area interests me a lot. Followed for more videos.