How a traditional algorithm is different from a machine learning algorithm? In this short post, I am sharing my understanding on the same.

A typical traditional algorithm takes input and produces output based solely on the input and the logical rules incorporated within the algorithm. Consequently, traditional algorithms heavily rely on human-defined rules. By applying architectural patterns and practices, the output can be controlled. These algorithms are more oriented towards interpretation and adhere to a logical/mathematical approach. Unlike machine learning algorithms, traditional algorithms lack the ability to learn autonomously, instead, they depend solely on predefined rules.

On the other hand, a machine learning (ML) algorithm takes input and output data to create a model. Unlike traditional algorithms, ML algorithms rely on the data provided rather than predefined rules. They excel in situations where the problem at hand requires prediction or learning patterns from the data. Leveraging the provided data, ML algorithms can adapt and improve their performance over time, making them particularly effective in predictive tasks.

Let's understand this concept with a simple example. Suppose we are building an email application with basic features like Send, Receive, Draft, and Attachment. In this scenario, a traditional algorithm would suffice for creating the application. However, if we want to implement a more advanced feature like spam detection, we may need to consider using a machine learning (ML) algorithm. To enable spam detection, two essential components are required: the dataset of received emails (raw data) and the desired output for each email, which can be either labelled as Yes (Spam) or No (Not a Spam). The challenge here lies in how the ML algorithm can effectively distinguish between spam and non-spam emails. A spam-detection algorithm must filter out spam emails while avoiding flagging non-spam messages that users want to see in their inbox. This requires the machine learning model to analyse the data, learn from it, and make accurate predictions. One efficient and fast ML algorithm commonly used for spam detection is Naïve Bayes. It provides reliable results and plays a crucial role in accurately identifying spam emails from legitimate ones.