140 Machine Learning Formulas
By Rubens Zimbres. Rubens is a Data Scientist, PhD in Business Administration, developing Machine Learning, Deep Learning, NLP and AI models using R, Python and Wolfram Mathematica. Click here to check his Github page.
Extract from the PDF document
This is a 17 page PDF document featuring a collection of short, one-line formulas covering the following topics (and more):
- Naive Bayes
- Mixture Models
- EM Algorithm
- Maximum Likelihood
- Bayesian Networks
- k-NN, weighted NN
- Logistic Regression
- Principal Component Analysis
- Stochastic Gradient
- Neural Networks
- Collaborative Filtering
- Back Propagation
- Support Vector Machines
- Ridge and Lasso Regression
It does look like a cheat sheet with everything you need to know in terms of formulas used in standard machine learning algorithms. It would be helpful if someone could add some meat to this document, in particular, adding some context and explanations for each formula. Many of these formulas are nicely illustrated here (scroll to the bottom of the page after clicking on the link.)
Click here to view the PDF document.
Top DSC Resources
- Article: Difference between Machine Learning, Data Science, AI, Deep Learnin...
- Article: What is Data Science? 24 Fundamental Articles Answering This Question
- Article: Hitchhiker's Guide to Data Science, Machine Learning, R, Python
- Tutorial: Data Science Cheat Sheet
- Tutorial: How to Become a Data Scientist - On Your Own
- Categories: Data Science - Machine Learning - AI - IoT - Deep Learning
- Tools: Hadoop - DataViZ - Python - R - SQL - Excel
- Techniques: Clustering - Regression - SVM - Neural Nets - Ensembles - Decision Trees
- Links: Cheat Sheets - Books - Events - Webinars - Tutorials - Training - News - Jobs
- Links: Announcements - Salary Surveys - Data Sets - Certification - RSS Feeds - About Us
- Newsletter: Sign-up - Past Editions - Members-Only Section - Content Search - For Bloggers
- DSC on: Ning - Twitter - LinkedIn - Facebook - GooglePlus
Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge
Comment
-
Comment by Richard Kenyon on February 21, 2017 at 12:48pm -
I have been playing with https://en.wikipedia.org/wiki/Tf%E2%80%93idf
I think it qualifies as a machine learning algorithm and can be usefully combined with Bayes methods.
Used iteratively it helps maintain a list of weight for 'tokens' being managed.
-
Comment by Ho Yin Chan on February 3, 2017 at 6:09pm -
It would nice to have linear discriminant analysis (LDA) after Principal component analysis (PCA), and probably hyperbolic tangent (tanh) and rectified linear unit function (relu) after sigmoid, and perhaps Kullback–Leibler divergence as well ? :)
-
Comment by Rubens Zimbres on January 30, 2017 at 4:49am -
Thanks for sharing, Vincent !
Follow Us
Top Content
Announcements
Videos
Resources
© 2018 Data Science Central™
Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
You need to be a member of Data Science Central to add comments!
Join Data Science Central