Which machine learning algorithm should I use? The ML Algorithm cheat sheet helps you choose the best machine learning algorithm for your predictive analytics solution. Your decision is driven by both the nature of your data and the goal you want to achieve with your data. What do you want to do with your data? I must state here that we need to have a solid understanding of the iterative system of methods that guide Data Scientists on the ideal approach to solving problems using the Data Science Methodology.

In supervised learning, each data point is labeled or associated with a category or value of interest. An example of a categorical label is assigning an image as either a'cat' or a'dog'. An example of a value label is the sale price associated with a used car. The goal of supervised learning is to study many labeled examples like these, and then to be able to make predictions about future data points. For example, identifying new photos with the correct animal or assigning accurate sale prices to other used cars.

In supervised learning, each data point is labeled or associated with a category or value of interest. An example of a categorical label is assigning an image as either a'cat' or a'dog'. An example of a value label is the sale price associated with a used car. The goal of supervised learning is to study many labeled examples like these, and then to be able to make predictions about future data points. For example, identifying new photos with the correct animal or assigning accurate sale prices to other used cars.

The Microsoft Azure Machine Learning Algorithm Cheat Sheet helps you choose the right algorithm for a predictive analytics model. Azure Machine Learning Studio has a large library of algorithms from the regression, classification, clustering, and anomaly detection families. Each is designed to address a different type of machine learning problem. Download the cheat sheet here: Machine Learning Algorithm Cheat Sheet (11x17 in.) Download and print the Machine Learning Algorithm Cheat Sheet in tabloid size to keep it handy and get help choosing an algorithm.

If we missed some popular cheat sheets, add them in the comments below. Data science is a multi-disciplinary field. Thus, there are thousands of packages and hundreds of programming functions out there in the data science world! An aspiring data enthusiast need not know all. A cheat sheet or reference card is a compilation of mostly used commands to help you learn that language's syntax at a faster rate.