5 Essential Machine Learning Algorithms For Business Applications

Businesses, from market giants like Amazon and Netflix to small retail stores somewhere in the heart of Ohio, strive to grow and improve their efficiency. Incorporating AI and Machine Learning into operational activity is one of the ways to achieve this. MobiDev engineers reviewed basic essential Machine Learning algorithms, explained their business application, and highlighted a step-by-step guide to choosing an appropriate algorithm that will meet your business needs.  

1. Regression

The goal of regression techniques is typically to explain or predict a specific numerical value while using historical data. It has found a wide application in areas such as retail, business process optimization, recommendation systems, etc. 

 Simple linear and multiple linear regression are the most popular among the variety of models. 

2. Classification

Classification is an ML algorithm for categorizing unstructured or structured data. Its application remains effective in such areas as spam filtering, document classification, auto-tagging, and defect detection. By analyzing the input, the model learns how to classify new information, mapping labels or targets to the data.

3. Clustering 

This ML method allows us to identify and group data points in organized structures. Clustering Machine Learning models differ depending on the approach. In business, these algorithms are used for anomaly detection, image segmentation, social network analysis, improving marketing campaigns, and fraud detection.

4. Deep Learning

Being a branch of ML, deep learning substitutes algorithms that lie on multi-layer neural networks but differ from traditional AI/ML techniques (see the picture below). The key difference is that deep learning models do not require data with a set of relevant features. DL is capable of text summarization, new image generation, speech-to-text conversion, emotion detection, and movement recognition.

5. Dimensionality Reduction

Thanks to the dimensionality reduction, we are able to shorten the duration of the training, shun overfitting, and apply the algorithm for data preparation performed prior to modeling. Business usage of dimensionality reduction isn’t limited to data preparation before modeling and includes the following areas: visualization of high-dimensional data, image compression, model runtime optimization, and reducing model complexity.