Primary Supervised Learning Algorithms Used in Machine Learning by Kevin Vu via @kdnuggets

In this tutorial, they are going to list some of the most common algorithms that are used in supervised learning along with a practical tutorial on such algorithms.

This is really useful and worth a bookmark or printout.

10 Regression Metrics Data Scientist Must Know (Python-Sklearn Code Included) by T Z J Y via @Medium

A great article that definitely needs to be added to your notes and kept for reference. I've printed it and put it in a folder and also added it to my Evernote so I can refer back to it when needed.

How to Run 40 Regression Models with a Few Lines of Code by Ismael Araujo via TDataScience

Learn how to run over 40 machine learning models using Lazy Predict for regression projects.

This is a real timesaver and very useful if you hadn't come across it before.

All Machine Learning Algorithms You Should Know in 2021 by Terence Shin via @TDataScience

Many machine learning algorithms exist that range from simple to complex in their approach, and together provide a powerful library of tools for analyzing and predicting patterns from data. If you are learning for the first time or reviewing techniques, then these intuitive explanations of the most popular machine learning models will help you kick off the new year with confidence.

This will help you get your machine learning right by using the correct algorithm.

A Tour of The Top 10 Algorithms for Machine Learning Newbies by James Le via @kdnuggets

For machine learning newbies who are eager to understand the basic of machine learning, here is a quick tour on the top 10 machine learning algorithms used by data scientists.

This is great and definitely worth a bookmark.  Please note this is over 2 pages.

Which machine learning algorithm should I use? by Hui Li via @SASsoftware

Here's an introduction to machine learning algorithms that can help beginners determine which algorithms to use to solve their specific problems.

This is an incredible resource from SAS in one of their blogs. The cheat sheet is priceless and you really MUST bookmark this.

Predictive Analytics 101 by @data36_com

If you have basic R or Python skills, you can build a simple predictive model. These two posts show you how:

Part one

Part two

I recommend you sign up for his newsletter here

Machine Learning Crash Courseby By Daniel Geng and Shannon Shih via ML@B @BerkeleyMl

A visual and easy to follow 2 part course in Machine Learning from Berkley.

Part 1 - Introduction, Regression/Classification, Cost Functions, and Gradient Descent

Part 2 - Perceptrons, Logistic Regression, and SVMs

These are brilliant and very useful if you want to understand the basics without spending large amounts of time.

I would advise following them too by clicking on the 3 horizontal bars at the LH top of the screen.

Top Algorithms and Methods Used by Data Scientists by Gregory Piatetsky,via @kdnuggets

Latest KDnuggets poll identifies the list of top algorithms actually used by Data Scientists, finds surprises including the most academic and most industry-oriented algorithms.

I'm surprised at some of the positions like RandomForest is so low in the list.

5 step checklist of multiple linear regression via [Data-Mania.com]

Read this excellent checklist and all the help around it from Data-Mania.  If you are not signed up to her site I strongly recommend that you do.

7 Types of Regression Techniques you should know! via @AnalyticsVidhya

Here are the 7 types of regression techniques that a data scientist should know from the blog on Analytics Vidhya.  Great blog and well worth a read.

Regression Analysis using R

While dealing with any prediction problem, the easiest, most widely used yet powerful technique is the Linear Regression. Regression analysis is used for modeling the relationship between a response variable and one or more input variables.

This blog from +suresh kumar Gorakala is great at talking through examples and providing R code which could help anyone still learning the basics.  It's actually good for the rest of us to remind us too.

10 types of regression - which one to use?

This from +Vincent Granville's blog gives some really great help on which one to use.