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.

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.

"Google's self-driving cars and robots get a lot of press, but the company's real future is in machine learning, the technology that enables computers to get smarter and more personal" Machine Learning is a form of Artificial Intelligence (AI) which allows computers to learn by way of observation and experience, rather than rigid pre-programming. These factors are the reason why Machine Learning is much more important nowadays than it was in the 50s. Selecting the right algorithm is a key part of any Machine Learning project, and because there are dozens to choose from, understanding their strengths and weaknesses in various business applications is essential. Machine Learning algorithms can predict patterns based on previous experiences. These algorithms find predictable, repeatable patterns that can be applied to e-commerce, data management, and new technologies such as driverless cars.

With numerous machine learning algorithms to experiment with, this relatively simple graph can quickly help you shortlist a handful of algorithms. Any then you may give Scikit a try. With numerous machine learning algorithms to experiment with, this relatively simple graph can quickly help you shortlist a handful of algorithms.

An interesting cheat sheet (a nice infographic!) was published by Microsoft sometime back to help beginning data scientists on how to choose a Machine Learning algorithm for different predictive analytics needs: Classification (to predict categories), Clustering (to discover structure), Regression (to predict values) and Anomaly Detection (to find unusual data points). Here's what Brandon, the author of the article "How to choose algorithms for Microsoft Azure Machine Learning", says about it: "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."