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Commonly used Machine Learning Algorithms

Intelligence itself is a lucrative concept and learning algorithms found great implications across industries wherever we can automate a process leveraging input from people and technology across verticals in all industries. The concept of making things intelligent has been driven under the umbrella of Artificial Intelligence with a subset of use cases getting fulfilled by Machine Learning (ML) and Deep Learning.

The approach to making a machine intelligent is driven by and implemented through a set of algorithms that can be implemented with the help of programming languages supporting native libraries and plugins. Predominantly Python and R codes have penetrated roots deeper in ML implementation since they have a rich library of analytics along with automation.

Machine Learning is further categorized into three forms of learning that are – Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Entire methods or use cases of Machine Learning falls under one of the three categories or may into the hybrid category.

This too much information about ML and its dynamics. Let us talk about the implementation approach.

Ideally, Python along with R is the preferred combination of programming languages to implement these algorithms since they are open source in nature with rich libraries and tools. The common algorithms getting implemented using Python and R with models available for training datasets are Linear regression algorithm, logistic regression algorithm, Decision tree algorithm, Support vector machine algorithm, Naïve Bayes algorithm, K-Nearest neighbor algorithm, K-means algorithm, Random forest algorithm.

Read more: Machine Learning is Your Secret Weapon for Customer Acquisition

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Apart from linear and logistic regression to solve other regression problems, there are other algorithms like stepwise regression, multivariate adaptive regression, and scatterplot smoothing regression.

The majority of Machine Learning problems are solved using Deep Learning techniques where predominantly Deep Boltzmann machine, deep belief network algorithm, convolutional neural network algorithm and stacked autoencoder algorithms are used to train models.

Problems, where we leverage the concept of neural network concepts, are solved using perception algorithm, Back-propagation algorithms, Hopfield network algorithms, and radius basis function network algorithms.

With the emergence of high power computing architecture along with python dimensionality reduction toolbox datasets of high dimension with multiple variables are trained using principal component analysis algorithm, partial least square regression algorithm, Sammon mapping algorithm, multidimensional scaling algorithm, projection pursuit algorithm, principal component regression algorithm, and discriminant analysis algorithms.

Holistically, Machine Learning is the umbrella of entire training and learning aspects of Artificial Intelligence and the ecosystem of Python along with R can be used diligently along with models, tools, and libraries to implement a broad set of algorithms to solve a broad set of problems across all category of Machine Learning 

Read more: 5 Innovative Applications of Automated Machine Learning

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