Machine Learning Tasks

With the use of machine learning, we can work on tasks that are too complex to solve by writing fixed programs. This is what makes machine learning even more interesting because understanding machine learning means understanding how to make our systems intelligent. In this article, I will explain to you what are the tasks that can be performed by Machine Learning.

Introduction To Machine Learning Tasks

In machine learning, a task is not the learning process. Learning is training which is the ability to perform a task. For example, if you want to train a robot to walk, walking is a task, so for that, we have to program a robot to walk.

Also, Read- 200+ Machine Learning Projects Solved and Explained.

Machine learning tasks are defined as how a trained system should handle an example. Here the example means data which is a collection of functionality which is collected by certain events. In the section below, I’ll introduce you to some of the most common categories of tasks performed by Machine Learning.

Tasks Performed By Machine Learning

There are so many tasks that can be done by machine learning. In this section, I’ll walk you through some of the most common tasks performed by Machine Learning.

Classification: In classification, we train an algorithm to specify the input category. There are two types of classification in Machine Learning:

  1. Binary classification
  2. Multiclass classification

An example of the problems where classification algorithms are used is object recognition, where our task is to take the digital code of the image as input and identify the object in the image as output.

Regression: In regression, we need to train an algorithm to predict a numerical value using historical and current data. One example of the problems where regression algorithms can be used in predicting the expected amount of claim a person will make from insurance and predicting stock prices. Regression algorithms are mainly used in algorithmic trading.

Transcription: In transcription, an algorithm is trained to observe unstructured data and transcribe it into textual form. An example of this task is an optical character recognition system where we train the algorithm to return the text present in the image.

Machine translation: In machine translation, an algorithm is trained to take one language as input and translate the text into another language. Google translate is a very good example of this task.

Anomaly Detection: In anomaly detection, an algorithm is trained to identify unusual activities in a process. An example of anomaly detection is the detection of fraud in credit card transactions. Using your purchase data, a bank can detect if someone is misusing your credit card.


So many things can be done using machine learning, but many of them fall into the categories mentioned above. The machine learning use cases I mentioned above are only meant to give you an idea of what problems machine learning can solve.

I hope you liked this article on what are the tasks that machine learning can solve. Feel free to ask your valuable questions in the comments section below.

Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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