Understanding Commonly Utilised Machine Learning Algorithms

Understanding Commonly Utilised Machine Learning Algorithms

Machine learning can be utilised in different fields from computational finance (like credit scoring) to image processing (like face recognition and motion detection), natural language processing (voice recognition), energy production, manufacturing, aerospace, and computational biology.

The need for processing massive data paved way to the creation of machine learning software. Many industries use this because it generally delivers quicker and more accurate results. Ultimately, it can help identify dangerous risks or profitable opportunities.

If you are beginning your way into data science, you need to choose the appropriate algorithm for your specific problem. To help you understand, here are the different types of machine learning algorithms:

Linear Regression

This is by far the simplest algorithm in machine learning. You can choose this algorithm if you want to predict some future value of a process, which is running at the moment. However, linear regression is unstable especially if the features are redundant.

Examples of where linear regression can be used include predicting sales of a specific product in the following month, predicting monthly gift card sales, the time going from one location to another and many more.

Logistic Regression

This will perform a binary classification. This means that the label outputs are binary. The logistic regression takes the linear combination of features and then applies a non-linear function. Considering this algorithm, you do not have to worry about the features being correlated because the algorithm will provide different ways to regularise the model.

Examples of where logistic regression can be used include predicting customer churn, measuring a marketing campaign’s effectiveness, fraud detection, and credit scoring.

Decision trees

This algorithm is similar to how people make a decision. Decision trees are easy to interpret. The algorithm can be used in compositions like Gradient boosting or Random forest. Examples of where decision trees can be used include investment decisions, bank loan defaulters, customer churn, and sales lead qualifications.

PCA (Principal Component Analysis)

PCA algorithm stipulates dimensionality reduction. If you come across a different range of features, which are highly correlated between each other, you can use this algorithm. You can also apply PCA for models that can overfit on a huge amount of data.

Naïve Bayes

The Naïve Bayes algorithm is easy to build. It can also be beneficial for massive data sets. You must know that Naïve Bayes is known to overtake highly sophisticated classification methods. With this, it is an ideal choice when memory and CPU are limiting.

Examples of where Naïve Bayes can be used include face recognition system, signifying if an email is spam or not, recommendation systems like Amazon and text classification.


When you look at it closely, K-means is more primal that is why it is also easy to understand the algorithm. You can use this as a baseline in different problems. The algorithm will help you assign labels according to the features of the objects. The biggest disadvantage of K-means is it needs to determine in advance how many clusters there will be in your data.

Final words

To make things easier, you should know where you stand at the onset so you can properly identify the right algorithms that are applicable and practical to implement.