1 Replies - 212 Views - Last Post: 26 October 2020 - 11:48 AM

#1 Patras   User is offline

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Dimensionality reduction techniques for machine learning

Posted 26 October 2020 - 11:04 AM

Hello everyone! As I said in a previous thread I'm an electrical engineer but I know studied some fundamentals of object programming in the past. I've been studying these days dimensionality reduction techniques like PCA, k-means clustering, agglomerative clustering and DBSCAN because I'm gonna use them in future. Do you know any other famous efficient in terms of precision ways easy to implement in python? I'm gonna have the samples I will work on soon but there won't be too much data so I'm not afraid of time performance of the algorithm but of its eventual lack of precision.

I know about supervised machine learning but I know nothing about neural networks. Anyway if there's something about it that is not very hard to understand I could try. Also if you know a good book on this thanks in advance! I've already read Introduction to Machine Learning with Python by A.C. Muller and S. Guido and it is good.

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Replies To: Dimensionality reduction techniques for machine learning

#2 Patras   User is offline

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Re: Dimensionality reduction techniques for machine learning

Posted 26 October 2020 - 11:48 AM

I forgot to mention that as you see from the examples, I'm looking for "unsupervised methods" because we won't have labels of all the samples (because of technical reasons) so we might need to separate some data in clusters.
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