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When To Use Machine Learning

ML engineering is the field of enabling software or computers to recognize patterns and characteristics in the provided data. The ML model learns from that data and then proceeds to identify the patterns in new data. Basically, a machine or computer self-learning keeps on improving its knowledge, which is used in different softwares etc,. This blog is to help you know where and when to use machine learning. 

What is machine learning

Before getting into using machine learning, let’s have a brief on what machine learning is! ML, short for Machine Learning, is a sub-branch of AI, which allows computers to learn on their own, based on the piece of code that they are working over. With time these machines or computers learn enough to recognize and identify patterns in the data. With more experiences they keep on learning more and improving the results they provide, which usually are predictions and statistics. 

When to use ML

According to machine learning experts, ML is not the solution to every problem or every type of problem. In fact, machine learning will only work its best if it is applied to the problem that it is designed for. In many cases you can build a robust and reliable system without ever using even the slightest touch of machine learning. For instance, using simple computations, boundary rules, and with little testing you can develop a system that will never need any type of data driven learning. 

Here are examples of when using machine learning would be a better choice than any other approach.

Defining the rules for a solution is not something we do in ML engineering. Tasks like determining whether an email is spam or not might seem super simple and easy for you, but when you start to write an ML software, the same thing becomes hard to pin-point. In such a situation, rule based solutions work better than anything. The factor of whether a specific email should be listed as spam or not, is inherent in the variety and large number of situations and conditions. Thus, target those conditions and follow the rules to solve them.

In a situation where you have to recognize patterns, it is best to use ML. For smaller businesses or a singular person, it is easy to detect and waste the spam emails, but with thousands of emails one cannot do the task efficiently and timely. For situations like these it is best to hire machine learning engineer and get the work done efficiently. 

It is beneficial to hire dedicated machine learning developers for manual entry tasks. Like the example above, manually entering loads of data does not only require more time but increases the chances of human error because of repetitive and tedious tasks. 

Here is a list of tasks that are better to handle with ML:

  • Product recommendation
  • Medical Diagnosis
  • Customer segmentation
  • Financial analysis
  • Predictive maintenance
  • Image recognition

Profile Selection

Machine learning is better to use for projects that require prediction based on some data or identifying trends and uncovering the logic behind it. In the beginning, limited data is used to train the model, which is also known as training data. Later on new data is provided to test the learning on. 

Supervised and unsupervised are two types of machine learning methods. They both use certain types of ML algorithms. Here are commonly used algorithms: linear regression, decision trees, support vector machines (SVMs), naïve Bayes, discriminant analysis, neural networks and ensemble methods.