Applications and drawbacks of Machine Learning

Before our readers can dive into the world of Machine Learning (ML), a very important difference needs to be pointed out. Machine learning is very different from Artificial Intelligence (AI) and these two terms cannot be substituted for the other. While both terms are related to computer science, ML is an application of AI. Artificial intelligence can be described as training computers to be better at tasks which normal humans do. As the term suggests, it’s a process of adding intelligence to computers. When we talk about machine learning in the most basic sense, it can be described as an implementation of AI wherein computers on their own can solve problems. This means they need not be programmed to carry out a particular task and  can learn from experience.

Now that the basic foundation for understanding ML has been laid, we can dive further into the discussion. This article will be divided into 3 parts:

-Brief information about ML

-Applications

-Drawbacks

The main aim of Machine learning is to give results. It is not concerned about the success and accuracy. The underlying theory behind ML is to create a program based on previously collected data. It allows the system to learn and improvise from the information previously collected. Whereas AI is concerned with finding the most ideal and optimized solution, Machine learning solely focuses on finding the solution. Data in ML plays a huge part as it is the lone source of information on which this application works. Data in ML can be split into three parts: training, validation and testing data. Training data and validation data is the information based on which the system is learning. It is concerned with training the model. Testing data, as the name suggests, is providing inputs to the system to predict values. It is used to evaluate if the system is providing the correct output.

Machine Learning Courses

Machine learning has vast applications in the current scenario. As ML is used to create new algorithms, it can be extensively used in SEO (Search Engine Optimization). SEO is used to increase the number of visitors to a website and is a part of online marketing wherein based on previous searches in web browsers, a person is shown related information or products. ML is also used in detecting and removing spam in mails or messages. Furthermore, it can be used in speech and image perception like Amazon Alexa and Google Assistant. The final part of this article deals with the drawbacks of ML. First and foremost, Machine learning cannot be trusted with giving accurate results. ML also requires large amounts of data and thereby large amounts of time and resources are needed. Machine learning no doubt has a bright future in the upcoming days. With the advancement of customer analytics, it will find further applications. It reduces human effort and has a large scope for continuous improvement. If you want to learn more about machine learning and its applications Then join a machine learning course now.

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