In this article, I will introduce you to a very important concept for machine learning practitioners: when do we need machine learning. It’s one of those basic issues that every computer science student faces when moving from basic computing practices to machine learning.
If you are one of those people who does not know when we should use programming and when do we use machine learning algorithms, I hope by the end of this article you will understand all about when do we use machine learning.
So What is Machine Learning?
Machine learning has become one of the most important topics within development organizations looking for innovative ways to leverage data assets to help the business gain a new level of understanding. Why add it to the mix? With the right ML models, companies can continuously predict changes in the business so they can better predict what’s next.
With data constantly being added, ML models ensure that the solution is constantly updated. The value is simple: If you use the most appropriate and constantly evolving data sources in the context of ML, you have the power to predict the future.
ML is a form of artificial intelligence that allows a system to learn from data rather than through explicit programming. However, using ML algorithms is not a simple process.
When Do We Need Machine Learning?
When do we need machine learning rather than directly programming our computers to do the job at hand? Two aspects of any given problem may require the use of programs that learn and improve based on their “experience”: the complexity of the problem and the need for adaptability.
Tasks That Are Too Complex to Program:
Tasks Done by Animals / Humans: There are many tasks that we humans perform regularly, but our introspection into how we do them is not elaborate enough to extract a well-defined agenda. Examples of such tasks include driving, speech recognition, and picture understanding.
In all of these tasks, advanced ML programs, programs that learn from experience, achieve quite satisfactory results when exposed to enough training examples.
Tasks beyond human capacities: Another set of tasks that gets a great benefit from ML algorithms is related to the analysis of a very large and complex data such as astronomical data, the transformation of medical records into medical knowledge, forecasting weather, genomic data analysis, web search engines and e-commerce.
With more and more digitally recorded data available, it is becoming evident that there are treasures of meaningful information buried in data archives that are far too large and complex for humans to understand. ML can easily extract meaningful patterns in large and complex data sets with very much promising results.
A limiting characteristic of programmed tools is their rigidity – once the program has been written and installed, it remains unchanged. However, many tasks change over time or from user to user. ML tools – programs whose behaviour adapts to their input data – offer a solution to these problems; they are by nature adaptive to changes in the environment with which they interact.
Some very successful applications of ML regarding such problems include applications that decode handwritten text, where a fixed program can easily adapt to different variations in handwriting from different users; spam detection programs, automatically adapting to changes in the nature of spam e-mails; and voice recognition programs.
I hope you now know the difference when we should do programming and when do we need to use Machine Learning. I hope you liked this article on when do we need machine learning. Feel free to ask your valuable questions in the comments section below.