Top 10 Commonly Used Machine Learning Algorithms

Decision Trees and Random Forests

Decision trees and random forests are two of the most commonly used machine learning algorithms. Decision trees are used to build models that can be used to make predictions about future events. Random forests are used to create models that can be used to predict the outcomes of future events.

Neural Networks

Neural networks are a type of machine learning algorithm that is used to model complex patterns in data. Neural networks are similar to other machine learning algorithms. Still, they are composed of a large number of interconnected processing nodes, or neurons, that can learn to recognise patterns of input data.

Boosting

Boosting is a machine learning technique that combines multiple weak learners to create a strong learner. Boosting is an iterative process where each weak learner is trained on a subset of the data. The aim is to correct the errors of the previous learner and build on top of it, creating a stronger and stronger learner.

K-Nearest Neighbors

The k-nearest neighbours (KNN) algorithm is one of the simplest and most commonly used machine learning algorithms. It is a non-parametric, lazy learning algorithm that can be used for both classification and regression. The KNN algorithm works by storing all training data points and then classifying new data points based on the similarity of their features.

Naive Bayes

Naive Bayes is a simple yet powerful machine-learning algorithm that can be used for both classification and regression tasks. It is a probabilistic algorithm that makes use of Bayesian inference to make predictions. The main advantage of using Naive Bayes is that it is relatively easy to implement and understand. Additionally, it has been shown to be quite effective in practice, especially with high-dimensional data sets.

Dimensionality Reduction

Dimensionality reduction is a type of unsupervised learning algorithm that can be used to reduce the number of features in a dataset. This can be useful for visualising data, increasing computational efficiency, or reducing the amount of data required for training. Common dimensionality reduction algorithms include Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

Model Ensembles

Ensemble learning is a powerful machine learning algorithm that combines the predictions of multiple models to produce more accurate results. Ensemble learning is often used in competition situations where the goal is to outperform other models or in cases where the individual models making up the ensemble are less accurate than we would like them to be.

The PG Certificate Programme in Data Science for Business Excellence and Innovation by IIM Nagpur is designed to equip working professionals with the skills and knowledge necessary to leverage data science in their organisations. The data science course covers a wide range of topics, from basic statistical concepts to more advanced machine learning algorithms. It also includes a significant amount of hands-on training so that participants can immediately start applying what they have learned to real-world problems.
The data science programme is taught by some of the most experienced and respected data scientists in the country, and has been designed keeping in mind the needs of the industry. Thus, it is ideal for working professionals who want to upskill themselves and stay ahead of the curve in this rapidly changing field.

Machine learning is a rapidly growing field with immense potential. However, there are still many common machine-learning algorithms that data scientists use on a daily basis. In this article, we have listed the top 10 commonly used machine learning algorithms along with their brief descriptions. We hope that this list was helpful for those who are just getting started in the field of machine learning.