Machine Learning Possibilities and Limitations

What we considered science fiction earlier is now a reality thanks to the growth of artificial intelligence, big data analytics, and new developments in machine learning in areas like neural networks, image recognition, speech recognition, computer vision, robotics, object detection, and natural language processing.

The objective of machine learning is to adjust independently to new data and produce judgments and suggestions based on many calculations and analyses. Such systems require little human involvement in their learning, pattern recognition, and decision-making processes. These machines are designed to boost efficiency and precision while virtually eliminating human error.

Today, machine learning engineers are one of the most in-demand professionals across the world. Such individuals have in-depth technical knowledge, including various AI concepts, data science, Python programming, and more. If you are worried about how to go about these concepts initially, then you can rely on a reputed Free ML course for beginners. It will teach you the basics of machine learning, post which you can decide which paid course to pursue and gain job-ready skills. 

This article throws light on the possibilities and limitations of machine learning.

Machine Learning Possibilities

Whenever you talk about any emerging technology, you first try to focus on its benefits. So, let us first go through what machine learning is capable of and how it can solve business problems.

First off, artificial intelligence and machine learning are resulting in the automation of various complex tasks. ML models can perform various tasks accurately with minimal errors and can work around the clock. Consider chatbots, for example. Customer support representatives have to answer repetitive queries on a daily basis. Chatbots, powered by machine learning, are now being used to address common customer problems without the need for human intervention. This is nothing but automation of a repetitive task and saves companies a lot of time and effort.

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Secondly, machine learning is finding applications in almost all industrial sectors, be it healthcare, manufacturing, retail, banking and finance, insurance, human resources, and even government agencies. The recommendation engines used in e-commerce sites are powered by ML as well as speech recognition, voice assistants, and autonomous cars are powered by this fascinating technology.

Machine learning is also used as a part of the data science lifecycle to identify hidden trends and patterns. Consider an ML model that forecasts consumer behavior in a certain market. By collecting historical data, the system can predict the weather forecast for further days. Moreover, the model will continue to use newly collected data to improve how it evaluates consumption patterns in addition to using prior data to generate forecasts for a certain period. 

Another interesting use of machine learning is for the purpose of fraud detection. The banking and finance, along with the insurance industry, find it most useful. The ML models can be employed to identify transactions that deviate from regular criteria, including the location of the user and purchase price, and can notify people when any unexpected activity happens.

Machine learning adoption is only in its infancy, and we have already witnessed some of its interesting applications. In the future, we will witness ML models being used to solve more complex human tasks.

Machine Learning Limitations

Before you decide to implement machine learning to solve real-world problems, you should be aware of its limitations.

As you know, machine learning works through certain algorithms. Now we can trust algorithms as they give us the desired results, but they come with ethical issues too. Bias can exist in algorithms at any stage of development. As there is human involvement during the creation of these algorithms, the bias associated with them can’t be completely eliminated.

Now, machine learning requires a large amount of hand-crafted and structured data for training purposes. There are times when we need to feed sensitive and important data to build an ML model. Here, data protection is an issue of concern, and there is a need for a proper control structure as it involves the rights of the people whose data the companies are processing.

Next, you must know that ML models perform better when they are fed with larger historical data sets and exposed for a longer period of time. This means a machine learning system cannot produce rapid, accurate predictions and is, in fact, time-consuming. For example, if you are creating an ML-powered chess game online, then it is important to feed the ML model with fresh data regularly so as to improve its judgments or predictions.

New ML models are created in research labs and promptly implemented in practical applications. However, even if the models are created to incorporate the most recent scientific advancements, they might not function in actual situations. This complex and expanding problem is that of a lack of reproducibility and is made worse by a lack of model testing procedures and open-source code. This issue can further impact the safety, dependability, and capacity to spot bias.  

We hope that you now have a clear understanding of what machine learning can and cannot do. If you are interested in stepping into this field for your future, then enroll in an online machine learning course and enhance your employment chances.

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