PROS and CONS of Machine Learning

                    Machine Learning

             "It is Defined as the field of study that gives  computers the        ability to learn without being explicitly   programmed."

• Arthur Samuel, an early American leader in the field of computer gaming 

•  artificial intelligence, coined the term “Machine Learning” in 1959 while at IBM

• Machine learning is programming computers to optimize a performance criterion using example data or past experience. 

• The model may be predictive to make predictions in the future, or descriptive to gain knowledge from data.

The field of study known as machine learning is concerned with the question      of how  to construct computer programs that automatically improve with               experience.

  What is Machine Learning?

          As a branch of artificial intelligence (AI) powered by science, machine     learning     gathers information through observation and real-world interaction to     make computers function like humans do and to optimize costs. With the help of an   IDE (Integrated development environment), its main goal is to make computers     work automatically without human intervention.

Pros of Machine Learning

Machine learning is an innovative tool that could change the landscape of doing work.
Here are some of its advantages:

1. No Human Intervention Needed

The preeminent benefit of machine learning is its instantaneous ability to adapt in the absence of human intervention. You can find common examples of machine learning in security and anti-virus software programs. It implements filters and other preventives in response to new threats.

The combination of AI technology and systems like machine learning strengthens security in distinguishing suspicious malware. It protects and neutralizes a computer against any virus by instigating appropriate measures. Additionally, it could clear the gap between the moment a new threat has been identified and its response time. 

Such near-immediate response is critical in a niche where hackers, bots, viruses, worms, another cyber-Threads could affect millions of people in minutes.

2. Identification of Trends and Patterns

One of the things machine learning can do is review and evaluate a wide range of data and use them to figure out trends not apparent to humans. Machine learning could understand peoples’ browsing behaviours and check their transaction histories to provide accurate products, deals, and reminders relevant to their needs. 

With this, companies can come up with suitable advertisements for their customers.

3. Wide Range of Applications

Investing in machine learning would be worth the money spent since it has a variety of applications and exceptional abilities to perform on its own without human help. You can practically apply this to any major field ranging from medical, business, banking, science, and many others. 

For the business field, machine learning plays a significant role in customer interaction and helps in businesses’ success. For medical-related works, machine learning can make things done efficiently by detecting disease quickly. As it further develops, machine learning could aid in a lot of endeavours in various fields.

4. Time and Energy-Efficient

Advanced technology’s primary goal is to minimize the time consumed in completing a specific task, and AI technology being has done numerous time-saving services.

This machine is programmed to adjust itself automatically, so it lessens the repairs needed due to system failure. Another benefit of using machine learning is it protects the environment from pollution and any other damages.

 

       Cons Of Machine Learning

        While machine learning could help a lot of industries, it has its downsides as well. Here            are some of its disadvantages:

      1. Needs Specialization for Every Project

          Different trades have systems needed to be tailored specially for them. For example,               healthcare has its specific medical procedures, just like how the manufacturing industry           has its production system. 

          Still, companies would need to hire well-skilled personnel to program or design a                      machine that fits their need. However, it’ll cost a significant amount of money, and it’s              time-consuming. Moreover, you may need various platforms for machine Learning to                come up with the specific result you’re looking for.

    2. High Error Susceptibility

      In developing machine learning, a tremendous amount of data is needed, and a                        significant   number of algorithms are tested. With it, there’s a possibility to meet                      numerous errors along the process, leading the users to several irrelevant trials.

      You can experience these mistakes several times as the machine gradually adjusts its              function. Although it’s an issue, it’s difficult to provide solutions to the root which caused            the errors.

    3. Lacks Good Quality Data

       To develop a high-quality machine learning that produces satisfactory output, it’ll need a         lot of programming. These data are crucial in developing the best versions of machines to        learn and act like humans that can produce desired results.

      Running several tests of data is a must to avoid having a biased system of decisions and       predictions. Moreover, if any errors are found initially, its subsequent events may be                 flawed. 

            For instance, a faulty dataset used in an e-commerce machine learning program                   could    result   in a system that recommends irrelevant products or utterly dissimilar to            the users’ history.

    4. Takes Time to Bring Results

     Since machine learning development needs a massive data set, it would take time for the         machine to occur and bring the results wanted, especially if you don’t have sufficient                 computing power. There may be times when the algorithm used, or interface developed           aren’t the appropriate ones you need.

     Running computer models consisting of high-volume data consumes a lot of computing           power and is quite costly. That’s why you have to identify and examine yourself whether           you have the resources such as time and money. 

 

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