AI/ML Cheat Sheets
-
AI/ML Cheat Sheets - A Pelican Blog
-
A Pelican Blog
[CV-Resume](https://mohcinemadkour.github.io/pdfs/mohcine_madkour_cv.pdf) [Categories](https://mohcinemadkour.github.io/categories.html)
AI/ML Cheat Sheets
By Mohcine Madkour, Fri 05 January 2018, in category Data science
Artificial intellignce, Data science
These AI/ML cheat sheets are a great way for beginners to get their mind oriented to the many possible ways machine learning and AI are accomplished from a data composition perspective. Each diagram reveals only the AI/ML algorithms that it implements; therefore taken together, they do not present an exhaustive list of the full AI/ML landscape. However, they will give a beginner a good overview of the landscape.
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
Kaggle runs programming contests to crowdsource machine learning solutions. Offers forums, a database of public datasets, tutorials, and machine learning job. Start with the Titanic challenge and grow from there.
Clustering
This is a wonderful page for introducing different clustering techniques with amazing charts:
http://scikit-learn.org/stable/modules/clustering.html
var disqus_shortname = 'leafyleap-2';
var disqus_identifier = 'AI/ML Cheat Sheets.html';
var disqus_url = 'https://mohcinemadkour.github.io/AI/ML Cheat Sheets.html';
(function() {
var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true;
dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js';
(document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq);
})();
Please enable JavaScript to view the comments.
Sitemap