In one of the articles, we had seen the fundamentals of Machine Learningand although briefly, but very concisely, we have also seen the types of machine learning and their applications. In this article, let’s try to go further deep into the various machine learning types, the way they are designed, the inputs and outputs involved, their application areas etc. for each of them.

About Machine Learning

Machine Learning is defined as a set of computer algorithms that makes systems autonomously learn and yield outputs and further improve from various analysis and outputs. Data will be fed to these algorithms, by which they automatically get trained to perform a certain task, get a certain output and hence we can apply that for our real-life business scenarios.

Machine Learning algorithms can be used to solve business problems like Regression, Classification, Forecasting, Clustering and Associations etc.

Based on the style and method involved, Machine Learning Algorithms are divided into four major types: Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, and Reinforcement Learning. In the coming sections, let’s drill-down into each of the algorithms.

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