Pros and cons of machine learning

The 20 Pros and Cons of Machine Learning You Must Know in 2023

Do you also think Machine Learning & Artificial intelligence is the same? You are missing out if you don’t know these features & pros and cons of machine learning Here we will try to develop an understanding of what is machine learning and how it works and also discuss on what are the pros and cons of using machine learning and try to find out a conclusion on whether we should use machine learning or not.

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Do you also think Machine Learning & Artificial intelligence is the same? You are missing out if you don’t know these features & pros and cons of machine learning

Here we will try to develop an understanding of what is machine learning and how it works and also discuss on what are the pros and cons of using machine learning and try to find out a conclusion on whether we should use machine learning or not.

What is Machine Learning?

We all have some understanding of artificial intelligence (AI), which is indeed the next big thing, which is going to revolutionize the whole world in the future to come and Machine learning is a branch of AI in collaboration with computer science, which focuses on using data and algorithm to imitate the way how the human learns and work and gradually improve the accuracy and due to the diverse possible usage of machine learning these 20 pros and cons of machine learning is very important to know.  

One of the most imperative companies which are devoted to developing machine learning is IBM, as one of the authors of IBM gets the credit to coin the word “machine learning”, during his inquiry around the game of checkers. The story is quite interesting, as in 1962, Robert Nealey a self-proclaimed checkers master, lost a game on an IBM 7094 computer and showed the whole world the power of machine learning and what the achievement possible with it, but it was only a beginning.

What's pros and cons of machine learning

It’s been a great journey since then and in the past few decades, the progress made in the field of machine learning is simply incomparable as we can see how a search engine like google can produce a search result within a fraction of seconds and the games have reached to the level of reality.

In the field of economics and statistics too, machine learning has a special holding, and it’s developing into a new field of education, “Data Science” as it’s related to the huge quantum of data and managing it, classifying it, and predicting the insights.

Another example of the use of machine learning and how it’s increasing in popularity is blockchain technology and how different cryptocurrencies are based on the technology and are considered more secure than the normal monetary transactions technology and that’s why the importance of machine learning is growing, so here we are going to discuss the 20 pros and cons of machine learning.

How Machine Learning Works

As per the explanation of UC Berkeley, the overall machine learning algorithm can be categorized into three different categories, which can make the understanding of machine learning a lot easier. 

How machine learning works

1. A Decision Process

There are many data and inputs and based on these labelled or unlabelled data the algorithm of machine learning works estimates a series of a pattern of data and probable action and delivers an output.

2. An Error Function

An error function makes a comparison to access the accuracy of the model based on some known examples and evaluates the prediction of the model. 

3. A Model Optimization Process

After passing through the decision process and an error function, if the model can fit better to the data points in the training and analyzing set, then all the sets are accorded weightage and are adjusted to reduce the disparity, between the known examples and the different kinds of the model estimates, and the process is repeated again and again, to obtain the most accurate and relevant output.

The overall working process of machine learning is very important to keep in mind before we start the discussion on the 20 pros and cons of machine learning as it will help in a better understanding of the system and come to an appropriate conclusion.

Machine Learning Methods

Just like the working of Machine learning can be categorized into three, similarly, the machine learning models can also be categorized into three primary categories.

1. Supervised Machine Learning        

Supervised machine learning, most commonly known as supervised learning, defines the use of labelled datasets to train algorithms to categorize data or predict outcomes accurately. When input data is fed into the model of machine learning, it adjusts the weightage of different estimates of possible outcomes until it has been fitted appropriately.

This befalls as part of the cross-validation process to ensure that the model avoids overfitting or underfitting so that any kind of contradiction can be avoided.

With the help of supervising learning various organizations can solve diverse problems that they have to face in real-world situations like classifying an e-mail into spam, promotion, or social. Based on the previous studies programs like neural networks, linear regression, naïve Bayes, logical regression, support vector machine, and random forest are the most used supervised machine learning.

Machine learning methods

2. Unsupervised Machine Learning

Unsupervised machine learning, most commonly known as using machine learning algorithm datasets to analyze and cluster unlabelled datasets, to provide the desired result.

There are many hidden patterns or data groupings that are discovered by unsupervised learning without the need for any kind of human interference. As this particular method can discover similarities and differences in information or datasets, it is an ideal method for explanatory data analysis, cross-selling strategies, image and pattern recognition, and customer segmentation.

Though there are many approaches for unsupervised learning, Principal component analysis (PCA) and Singular Value Decomposition are the two most common approaches used.

3. Semi-Supervised Learning 

As the name is suggesting it is a route taken in between the supervised and unsupervised machine learning methods, as it uses a labelled dataset to guide classification and unlabelled datasets to feature the extraction and it is most suitable in cases where supervised or unsupervised methods are unable to provide a proper solution.

As of now, we have gathered enough information related to machine learning and what are the diversities in machine learning now is the perfect time to start the discussion on the 20 pros and cons of machine learning.

Here are The 20 Ultimate Pros and Cons of Machine Learning You Must Know

Pros of Machine Learning

1. Analyzing the Pattern

There can be thousands of possibilities and probabilities but with machine learning, the pattern and data flow can be recognized with ease, after analyzing and interpreting the pile of data, and then predicting the result with greater accuracy, thereby producing the output.

Analyzing the pattern is an important section of studying the 20 pros and cons of machine learning as the entire system of machine learning is based on analyzing the possible outcomes and they produce the best possible result.

2. Step Towards Automation

One only thing that human needs to do with machine learning are to operate and train the system and the rest of the things are taken care of by the system itself and everything goes on an automation basis which is self-driven and self-reliant.

A very influential utility of Machine Learning is its ability to automate various decision-making tasks and frees up a lot of time for developers so that they can use their time for more productive use.

For example, with the help of the chatbot function added to our website, as soon as someone makes any comment or question regarding a product or a service, the chatbot reacts instantly, without any time constraints, and makes the user interfere with the website much better.

Check out to become a master of automation

3. Broad Application 

The diversity of machine learning has spread across every sector in the past few decades and it’s being utilized by entrepreneurs in every field like sales, operations, analysis, forecasting, etc, and that too with a lot of ease and without any flaws.

4. Data Handling

In the era of 2022, it is not wrong to say data is money, and with machine learning as a support system, it’s fun to work with massive data, which can be handled like a pinch of salt and there is nothing to be worried about.

5. Simultaneous Execution

If you were asked to do two work simultaneously, in most cases you will struggle to do so, but machine learning can perfectly do different kinds of jobs simultaneously.

6. Scope of Improvement 

The evolution process of machine learning and artificial intelligence is a never-ending process, as it has the potential to get upgraded to an infinite level, and as no one knows the future, we can never predict what it can attain.

Scope of improvement in machine learning

Machine Learning algorithms are capable of learning from the data we provide as and when new data is provided, the model’s accuracy and efficiency to make decisions advance with subsequent training which you can experience with trading giants like Amazon, Walmart, etc who collect a huge volume of new data every day and after analyzing the data they keep on improving their system regularly.

7. Open Opportunities

People with a creative mind and technical knowledge are blessed with incomparable opportunities in the job sector, as there is always a scarcity of new talents, and with a field that is growing at an accelerated pace chances of being successful increase.

Again it is important to know the pros and cons before you are deciding to go for a job related to machine learning it’s something you are going to deal with for the rest of your life, so better you enquire into it deeply and then make a decision.

8. Reduce Time and Complexity

Since the introduction of machine learning the time consumed to complete a task has been cut down miserably as with the help of machine learning any specific job can be completed in seconds, and with one hundred percent accuracy which is something impossible to achieve through human work.

Have you ever thought about how fashion trendy websites or product-selling websites can guess what you are looking for and as soon as your log in they show you products that you like more?

This advantage of machine learning is something we all are facing in day-to-day life but don’t know how it’s happening, as keeps us surprised. The algorithm of machine learning works in the backend and keeps a record of all our previous activities and by analyzing the previous activities they are capable to show us the probable outcomes.

10. Wide Range of Applications

Almost every industry and entrepreneur are now a day’s dependent on machine learning and it’s hard to find an exception to this. Start from government jobs like defense and banking to any private entrepreneur like reliance or airtel, a system like GPS tracking to e-mail filtering, from spelling and grammar checking to plagiarism checking, everything is possible through machine learning.

Cons of Machine Learning

1. Data Acquisition

The most exhausting fact about machine learning is the difficulty in the acquisition of data, and in a few cases, we may have to pay expensively to collect data. Another big problem is the error in data collection as when we collect data during a survey there are high chances that the data is a myth or the method used to collect data is inappropriate to the subject of study.

If there is an impurity or imbalance in data of any kind it leads to making the entire system respond poorly and inaccurately which is the greatest challenge and disadvantage of machine learning. When we are talking about the 20 pros and cons of machine learning, this is probably the greatest difficulty we might face during the build process of machine learning.

2. Highly Error-Prone

The data feed into the model of machine learning during the training and testing period of the entire ecosystem must be clean, accurate, and to the point related to the subject of study because the whole system of machine learning works on the principle of, “Garbage-in-Garbage-out”.

It means that if we are feeding false or improper data into the system during the training and modeling of machine learning, the output will also be the same and may not fulfill our need for the study.

3. Time-Consuming

It is easy for machine learning programs to process huge amount of data with a high degree of accuracy, but sometimes the volume of data becomes large enough that the server or the system is unable to process it, like it may take unusual time to process or it can be worst if the causes the system to crash during the processing time.

Another greatest difficulty to come across while studying the pros and cons of machine learning is the time factor as we have already discussed that there is no consistency with the time and it may take an undesirably long time and the whole essence of the machine learning program is lost.

4. Algorithm Selection

There are various algorithms on which a machine learning program can work, but it is tedious work to select the perfect algorithm which suits our program almost perfectly, which is a disadvantage especially when we are unable to choose the perfect algorithm.

Training a machine is not a child’s play and it requires a lot of coding knowledge as well as brilliancy and the efforts of an individual.

5. Inconsistency

Working with machine learning requires a lot of time-to-time updates, as the requirements keep on changing over time, and it during the process of updating the system it is equally important to maintain the consistency and homogeneity of the data.

If we make changes to the input data, there are chances we may mishap with the whole system and let it crash and the condition can be worst if after all this mishap we are unable to locate the bug in the system.

6. Time Taking

As we all know the entire process of creating a machine learning program that is based on artificial intelligence, takes a lot of time right from the feeding of data to the final testing stage, and sometimes it may take years to complete a single program.

Machine learning is time-consuming

7. Massive Resources 

To develop a complete machine learning program, we need a lot of resources, like data to feed, programmers to write code, a testing environment, and many more which sometimes ask for a lot of investment as well.

8. Excessive Use May Harm Mankind:

If we continue to depend too much on machine learning, we may lose our true identity and end up becoming highly dependent on machine learning and artificial intelligence and lose our identity as the wisest living being on the planet.

If we are planning to create a highly automated future, it may lead to massive unemployment and end up in complete chaos which is hard to revert.

9. Errors are Frequent and Take a Long Time 

It is common to have errors in the system, due to the continuous updates it receives, but sometimes it is difficult to rectify the error and it may take a longer time as well, and during the process of rectification and correction, it hampers the business and lead to a drop-in sale as well.

10. It is Expensive

The most important thing is the cost as it is not at all cheap, as the developer charges heavily to complete the task and also, we have to pay them constantly to keep the updates running on a timely basis.

There is also a lot of funding needed to complete the testing period as it works on a trial and error basis where the developer tries the system with varieties of algorithms and after facing failure several times when they finally taste success the bill has already mounted a lot.


We hope this article on machine learning is valuable to you and you must have got what you are looking for, and now let’s see some final words on machine learning.

Now as you must have known the pros and cons of machine learning, you are in a position to decide whether you should do it or not, as it is up to you if you are willing to join the race of technology or you want to keep yourself aloof from this race.

Machine Learning advantages and disadvantages as cited above will help the reader know a bit more about this technology and also, they will be able to decide what is better for their business.

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Digital Scholar

Digital Scholar is a premier agency-styled digital marketing institute in India. Which offers an online digital marketing course and a free digital marketing course worldwide to elevate their digital skills and become industry experts. Digital Scholar is headed by Sorav Jain and co-founder Rishi Jain, who are pioneers in the field of digital marketing. Digital Scholar’s blogs touch upon numerous aspects of digital marketing and help you get intensive ideas of different domains of digital marketing.

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