Once again an excellent post by Ajit Jaokar: Thanks Ajit!
Below is just the intro overview, the much longer post comes through when you click through to Linkedin. Nicely done, incudes as part of the taxonomy a number of typical usage descriptions.
Artificial Intelligence #5 : A taxonomy of machine learning and deep learning algorithms
Published on May 25, 2021 By Ajit Jaokar
Course Director: Artificial Intelligence: Cloud and Edge Implementations - University of Oxford
Like the Glossary I posted last week, there is no taxonomy for machine learning and deep learning algorithms.
Most ML/DL problems are classification problems, and a small subset of algorithms can be used to solve most of them (ex: SVM. Logistic regression or Xgboost). In that sense, a full taxonomy maybe an overkill. However, if you really want to understand something, you need to know acquire knowledge of a repertoire of algorithms – to overcome the known unknowns problem.
In this post, rather than present a taxonomy, I present a range of taxonomy approaches for machine learning and deep learning algorithms. Some of these are mathematical. If you are just beginning data science, start from the non-mathematical approaches to taxonomy. Don't be tempted to go for the maths approach. But if you have an aptitude towards maths, you should consider the maths approach because it gives you a deeper understanding. Also, I am a bit biased because many in my network in Oxford, MIT, Cambridge, Technion etc would also take a similar maths-based approach.
Finally, I suggest one specific approach to taxonomy which I like and find most complete. It is complex but it is free to download.
Taxonomy approaches
Firstly, the approach from Jason Brownlee is always a good place to start because its pragmatic and implementable in code in A tour of machine learning algorithms. Note that these are machine learning algorithms (not deep learning algorithms). A more visual approach is below source packt. .... "
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