The algorithms have been sorted into 9 groups: Anomaly Detection, Association Rule Learning, Classification, Clustering, Dimensional Reduction, Ensemble, Neural Networks, Regression, Regularization. In this post, you'll find 101 machine learning algorithms, including useful cheat sheets to help you know when to use each one (if available). At Data Science Dojo, our mission is to make data science (machine learning in this case) available to everyone. Whether you join our data science bootcamp, read our blog, or watch our tutorials, we want everyone to have the opportunity to learn data science. Having said that, each accordion dropdown is embeddable if you want to take them with you.

The answer to the question "What machine learning algorithm should I use?" is always "It depends." It depends on the size, quality, and nature of the data. It depends what you want to do with the answer. It depends on how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have. Even the most experienced data scientists can't tell which algorithm will perform best before trying them. The Microsoft Azure Machine Learning Algorithm Cheat Sheet helps you choose the right machine learning algorithm for your predictive analytics solutions from the Microsoft Azure Machine Learning library of algorithms.

Well, there is no straightforward and sure-shot answer to this question. The answer depends on many factors like the problem statement and the kind of output you want, type and size of the data, the available computational time, number of features and observations in the data, to name a few. It is usually recommended to gather a good amount of data to get reliable predictions. However, many a time the availability of data is a constraint. So, if the training data is smaller or if the dataset has a fewer number of observations and a higher number of features like genetics or textual data, choose algorithms with high bias/low variance like Linear regression, Naïve Bayes, Linear SVM.

Well, there is no straightforward and sure-shot answer to this question. The answer depends on many factors like the problem statement and the kind of output you want, type and size of the data, the available computational time, number of features, and observations in the data, to name a few. Here are some important considerations while choosing an algorithm. It is usually recommended to gather a good amount of data to get reliable predictions. However, many a time, the availability of data is a constraint.

Azure Machine Learning Studio comes with a large number of machine learning algorithms that you can use to solve predictive analytics problems. The infographic below demonstrates how the four types of machine learning algorithms – regression, anomaly detection, clustering, and classification – can be used to answer your machine learning questions. The Microsoft Azure Machine Learning Algorithm Cheat Sheet helps you choose the right machine learning algorithm for your predictive analytics solutions from the Microsoft Azure Machine Learning library of algorithms. To download the cheat sheet and follow along with this article, go to Machine learning algorithm cheat sheet for Microsoft Azure Machine Learning Studio. This cheat sheet is perfect for students its aimed at someone with undergraduate-level machine learning, trying to choose an algorithm to start with in Azure Machine Learning Studio.